Google Health research publications
Publishing our work allows us to share ideas and work collaboratively to advance healthcare. This is a comprehensive view of our publications and associated blog posts.
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Blog Posts [more at Google Keyword Blog & Google Research Blog]
Google’s vision for a healthier futureby Karen DeSalvo
Google Keyword Blog | 1-Nov-2024
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Blog Posts
How gen AI can help doctors and nurses ease their administrative workloadsby Aashima Gupta
Google Keyword Blog | 17-Oct-2024
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Blog Posts
AI startups revolutionizing mental health careby Karen DeSalvo
Google Keyword Blog | 10-Oct-2024
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Blog Posts
Supporting India’s digital health transformationby Bakul Patel
Google Keyword Blog | 3-Oct-2024
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Blog Posts
4 principles to guide AI in supporting mental healthby Megan Jones Bell
Google Keyword Blog | 8-Aug-2024
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Blog Posts
Google Research at Google I/O 2024by Yossi Matias & James Manyika
Google Research Blog | 24-May-2024
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Blog Posts
How 7 businesses are putting Google Cloud’s AI innovations to workby Carrie Tharp
Google Keyword Blog | 4-Apr-2024
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Blog Posts [more at Google Keyword Blog & Google Research Blog]
How we’re using AI to connect people to health informationby Karen DeSalvo
Google Keyword Blog | 19-Mar-2024
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Blog Posts
How AI is helping advance women’s health around the worldby Ronit Levavi Morad & Preeti Singh
Google Keyword Blog | 8-Mar-2024
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Blog Posts
A new commitment to digital wellbeing for kids and teensby Karen DeSalvo
Google Keyword Blog | 6-Feb-2024
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Blog Posts
3 predictions for AI in healthcare in 2024by Aashima Gupta
Google Keyword Blog | 9-Jan-2024
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Blog Posts
2023: A year of groundbreaking advances in AI and computingby Jeff Dean, James Manyika, & Demis Hassabis
Google Research Blog | 22-Dec-2023
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Blog Posts
23 of our biggest moments in 2023by Molly McHugh-Johnson
Google Keyword Blog | 20-Dec-2023
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Blog Posts
4 ways we think about health equity and AIby Ivor Horn
Google Keyword Blog | 2-Nov-2023
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Blog Posts
5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia Shravya Shetty
Google Keyword Blog | 31-Oct-2023
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Blog Posts
How we’re using AI to help transform healthcareby Yossi Mattias
Google Keyword Blog | 23-Oct-2023
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Blog Posts
A new collaboration to improve nutrition informationby Nira Goren
Google Keyword Blog | 18-Oct-2023
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Blog Posts
HLTH 2023: Bringing AI to health responsiblyby Michaell Howell
Google Keyword Blog | 9-Oct-2023
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Blog Posts
How AI can improve health for everyone, everywhereby Karen DeSalvo
Google Keyword Blog | 3-Oct-2023
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Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
Google Keyword Blog | 18-Jul-2023
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Blog Posts [more at Google Keyword Blog & Google AI Blog]
Looking to the next 75 years of the NHSby Susan Thomas
Google Keyword Blog | 5-Jul-2023
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Blog Posts
New research from the UK focused on technology’s role in healthcareby Susan Thomas
Google Keyword Blog | 13-Jun-2023
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Blog Posts
Our collaboration with WHO to improve public healthby Karen DeSalvo
Google Keyword Blog | 23-May-2023
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Blog Posts
Partnering with startups using AI to improve healthcareby Karen DeSalvo
Google Keyword Blog | 22-May-2023
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Blog Posts
More mental health resources for the moments you need themby Megan Jones Bell
Google Keyword Blog | 15-May-2023
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Blog Posts
3 ways Google products can help you feel less stressedby Megan Jones Bell
Google Keyword Blog | 13-Apr-2023
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Blog Posts
New ways we’re helping people live healthier livesby Karen DeSalvo
Google Keyword Blog | 14-Mar-2023
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Blog Posts
Our latest health AI research updatesby Greg Corrado & Yossi Matias
Google Keyword Blog | 14-Mar-2023
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Blog Posts
Google Research, 2022 & beyond: Healthby Greg Corrado & Yossi Matias
Google Research Blog | 23-Feb-2023
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Blog Posts
Meet our Health Equity Research Initiative awardeesby Ivor Horn
Google Keyword Blog | 26-Jan-2023
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
Google Keyword Blog | 17-Jan-2023
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Blog Posts
3 ways to take better care of your mind and body in 2023by Megan Jones Bell
Google Keyword Blog | 5-Jan-2023
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Blog Posts
8 things we launched in 2022 to support your healthby Iz Conroy
Google Keyword Blog | 21-Dec-2022
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Blog Posts
How to use Google Search to help manage uncertain timesby Hema Budaraju
Google Keyword Blog | 14-Dec-2022
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Blog Posts
Unlocking the potential of technology to support healthby Karen DeSalvo
Google Keyword Blog | 15-Nov-2022
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Blog Posts
Healthy collaboration: Why partnerships are the heart of healthcare innovationby Aashima Gupta
Google Cloud Blog | 14-Nov-2022
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Blog Posts
3 ways AI is scaling helpful technologies worldwideby Jeff Dean
Google Keyword Blog | 2-Nov-2022
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Blog Posts
Democratizing access to healthby Karen DeSalvo
Google Keyword Blog | 27-Oct-2022
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Blog Posts
Google Assistant offers information and hope for Breast Cancer Awareness Monthby Riva Sciuto
Google Keyword Blog | 19-Oct-2022
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Blog Posts
Our work toward health equityby Ivor Horn
Google Keyword Blog | 12-Sep-2022
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Blog Posts
Dr. Von Nguyen’s temperature check on public healthby Lauren Winer
Google Keyword Blog | 25-Aug-2022
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Blog Posts
Suicide prevention resources on Google Searchby Anne Merritt
Google Keyword Blog | 20-Jul-2022
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Blog Posts
Mental health resources you can count onby Megan Jones Bell
Google Keyword Blog | 17-May-2022
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Blog Posts
Raising awareness of the dangers of fentanylby Megan Jones Bell & Garth Graham
Google Keyword Blog | 10-May-2022
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Blog Posts
The Check Up: helping people live healthier livesby Karen DeSalvo
Google Keyword Blog | 24-Mar-2022
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
Google Research Blog | 24-Mar-2022
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Blog Posts
Extending Care Studio with a new healthcare partnershipby Paul Muret
Google Keyword Blog | 15-Mar-2022
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Blog Posts
Take a look at Conditions, our new feature in Care Studioby Paul Muret
Google Keyword Blog | 8-Mar-2022
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Blog Posts
Google Research: Themes from 2021 and Beyondby Jeff Dean
Google Research Blog | 11-Jan-2022
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Blog Posts
Making healthcare options more accessible on Searchby Hema Budaraju
Google Keyword Blog | 2-Dec-2021
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Blog Posts
HLTH: Building on our commitments in healthby Karen DeSalvo
Google Keyword Blog | 17-Oct-2021
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Blog Posts
When it comes to mental health, what are we searching for?by Alicia Cormie
Google Keyword Blog | 6-May-2021
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Blog Posts
Dr. Ivor Horn talks about technology and health equityby Alicia Cormie
Google Keyword Blog | 16-Apr-2021
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Blog Posts
Our Care Studio pilot is expanding to more cliniciansby Paul Muret
Google Keyword Blog | 23- Feb-2021
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Blog Posts
Google Research: Looking Back at 2020, and Forward to 2021by Jeff Dean
Google Research Blog | 12-Jan-2021
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Blog Posts
A new Google Search tool to support women with postpartum depressionby David Feinberg
LinkedIn Blog | 8-Dec-2020
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Blog Posts
Prepare for medical visits with help from Google and AHRQby Dave Greenwood
Google Keyword Blog | 2-Dec-2020
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Blog Posts
A Collaborative Approach to Shaping Successful UX Critique Practicesby Anna Lurchenko
Google Design Blog | 29-Jul-2020
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Blog Posts
Learn more about anxiety with a self-assessment on Searchby Daniel Gillison, Jr
Google Keyword Blog | 28-May-2020
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Blog Posts
Google Research: Looking Back at 2019, and Forward to 2020 and Beyondby Jeff Dean
Google Research Blog | 9-Jan-2020
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Blog Posts
Lessons Learned from Developing ML for Healthcareby Yun Liu & Po-Hsuan Cameron Chen
Google Research Blog | 10-Dec-2019
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Blog Posts
Tools to help healthcare providers deliver better careby David Feinberg
Google Keyword Blog | 20-Nov-2019
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Blog Posts
Breast cancer and tech...a reason for optimismby Ruth Porat
Google Keyword Blog | 21-Oct-2019
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Blog Posts
DeepMind’s health team joins Google Healthby Dominic King
Google Keyword Blog | 18-Sep-2019
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Blog Posts
Looking Back at Google’s Research Efforts in 2018by Jeff Dean
Google Research Blog | 15-Jan-2019
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Blog Posts
Meet David Feinberg, head of Google Healthby Google
Google Keyword Blog | 17-Jun-2019
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Blog Posts
AI for Social Good in Asia Pacificby Kent Walter
Google Keyword Blog | 13-Dec-2018
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Blog Posts
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)by Jeff Dean
Google Research Blog | 12-Jan-2018
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Blog Posts
Gain a deeper understanding of Posttraumatic Stress Disorder on Googleby Paula Schnurr & Teri Brister
Google Keyword Blog | 5-Dec-2017
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Blog Posts
Learning more about clinical depression with the PHQ-9 questionnaireby Mary Giliberti
Google Keyword Blog | 23-Aug-2017
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Blog Posts
Partnering on machine learning in healthcareby Katherine Chou
Google Research Blog | 17-May-2017
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Blog Posts
The Google Brain Team — Looking Back on 2016by Jeff Dean
Google Research Blog | 12-Jan-2017
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COVID-19 Blog Posts
Supporting evolving COVID information needsby Hema Budaraju
Google Keyword Blog | 16-Jun-2022
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COVID-19 Blog Posts [more at Google Keyword Blog]
Group effort: How we helped launch an NYC vaccine siteby Lauren Gallagher
Google Keyword Blog | 11-Feb-2022
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COVID-19 Blog Posts [more at Google Keyword Blog]
This year, we searched for ways to stay healthyby Hema Budaraju
Google Keyword Blog | 8-Dec-2021
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COVID-19 Blog Posts
New tools to support vaccine access and distributionby Tomer Shekel
Google Keyword Blog | 9-Jun-2021
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COVID-19 Blog Posts
An update on our COVID response prioritiesby the COVID Response team, Google India
Google India Blog | 10-May-2021
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COVID-19 Blog Posts
Our commitment to COVID-19 vaccine equityby Karen DeSalvo
Google Keyword Blog | 15-Apr-2021[Spanish version]
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COVID-19 Blog Posts
How anonymized data helps fight against diseaseby Stephen Ratcliffe
Google Keyword Blog | 24-Feb-2021
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COVID-19 Blog Posts
How we’re helping get vaccines to more peopleby Sundar Pichai
Google Keyword Blog | 25-Jan-2021
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COVID-19 Blog Posts
Exposure Notifications: end of year updateby Steph Hannon
Google Keyword Blog | 11-Dec-2020
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COVID-19 Blog Posts
How you'll find accurate and timely information on COVID-19 vaccinesby Karen DeSalvo & Kristie Canegallo
Google Keyword Blog | 10-Dec-2020
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COVID-19 Blog Posts
How I’m giving thanks (and staying safe) this Thanksgivingby Karen DeSalvo
Google Keyword Blog | 24-Nov-2020 [Spanish version]
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COVID-19 Blog Posts
A Q&A on coronavirus vaccinesGoogle Keyword Blog
10-Nov-2020
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COVID-19 Blog Posts
An update on our efforts to help Americans navigate COVID-19by Ruth Porat
Google Keyword Blog | 27-Oct-2020
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COVID-19 Blog Posts
Making data useful for public healthby Katherine Chou
Google Keyword Blog | 17-Sept-2020
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COVID-19 Blog Posts
Google supports COVID-19 AI and data analytics projectsby Mollie Javerbaum & Meghan Houghton
Google Keyword Blog | 10-Sep-2020
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COVID-19 Blog Posts
Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
Google Keyword Blog | 2-Sep-2020
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COVID-19 Blog Posts
An update on Exposure Notificationsby Dave Burke
Google Keyword Blog | 31-Jul-2020
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COVID-19 Blog Posts
Exposure Notification API launches to support public health agenciesby Apple & Google
Google Keyword Blog | 20-May-2020
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COVID-19 Blog Posts
Dr. Karen DeSalvo on ‘putting information first’ during COVID-19by Megan Washam
Google Keyword Blog | 13-May-2020
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COVID-19 Blog Posts
Resources for mental health support during COVID-19by David Feinberg
Google Keyword Blog | 8-May-2020
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COVID-19 Blog Posts
Helping you avoid COVID-19 online security risksGoogle Africa Blog
Google Africa Blog | 23-Apr-2020
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COVID-19 Blog Posts
Apple and Google partner on COVID-19 contact tracing technologyby Apple & Google
Google Keyword Blog | 10-Apr-2020
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COVID-19 Blog Posts
Connecting people to virtual care optionsby Julie Black
Google Keyword Blog | 10-Apr-2020
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COVID-19 Blog Posts
Support for public health workers fighting COVID-19by Karen DeSalvo
Google Keyword Blog | 6-Apr-2020
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COVID-19 Blog Posts
Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
Google Keyword Blog | 3-Apr-2020
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COVID-19 Blog Posts
Connecting people with COVID-19 information and resourcesby Emily Moxley
Google Keyword Blog | 21-Mar-2020
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COVID-19 Blog Posts
COVID-19: How we’re continuing to helpby Sundar Pichai
Google Keyword Blog | 15-Mar-2020
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COVID-19 Blog Posts
Coronavirus: How we’re helpingby Sundar Pichai
Google Keyword Blog | 6-Mar-2020
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Reviews
Safety principles for medical summarization using generative AIObika, D., Kelly, C., Ding, N., Farrance, C., Krause, J., Mittal, P., Cheung, D., Cole-Lewis, H., Elish, M., Karthikesalingam, A., Webster, D., Patel, B. & Howell, M.
Nat. Med. 1–3 (2024). [readcube]
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Reviews
A multiparty collaboration to engage diverse populations in community-centered artificial intelligence researchDevon-Sand, A., Sayres, R., Liu, Y., Strachan, P., Smith, M. A., Nguyen, T., Ko, J. M. & Lin, S.
Mayo Clinic Proceedings: Digital Health 2, 463–469 (2024).
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Reviews
The Opportunities and Risks of Large Language Models in Mental HealthLawrence, H. R., Schneider, R. A., Rubin, S. B.,Matarić, M. J., McDuff, D. J. & Jones Bell, M.
JMIR Ment Health 11, e59479 (2024).
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Reviews
The Regulation of Clinical Artificial IntelligenceBlumenthal David & Patel Bakul.
NEJM AI 1, AIpc2400545 (2024).
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Reviews
Generative artificial intelligence, patient safety and healthcare quality: a reviewHowell, M. D.
BMJ Qual. Saf. (2024).
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Reviews
AI in Action: Accelerating Progress Towards the Sustainable Development GoalsGosselink, B. H., Brandt, K., Croak, M., DeSalvo, K., Gomes, B., Ibrahim, L., Johnson, M., Matias, Y., Porat, R., Walker, K. & Manyika, J.
arXiv [cs.CY] (2024).
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Reviews
Transforming Public Health Practice With Generative Artificial IntelligenceBharel, M., Auerbach, J., Nguyen, V. & DeSalvo, K. B.
Health Aff. 43, 776–782 (2024).
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Reviews
Information is a determinant of healthGraham, G., Goren, N., Sounderajah, V. & DeSalvo, K.
Nat. Med. (2024). [readcube]
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Reviews
An intentional approach to managing bias in general purpose embedding modelsWeng, W.-H., Sellergen, A., Kiraly, A. P., D’Amour, A., Park, J., Pilgrim, R., Pfohl, S., Lau, C., Natarajan, V., Azizi, S., Karthikesalingam, A., Cole-Lewis, H., Matias, Y., Corrado, G. S., Webster, D. R., Shetty, S., Prabhakara, S., Eswaran, K., Celi, L. A. G. & Liu, Y.
The Lancet Digital Health 6, e126–e130 (2024).
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Reviews
Three Epochs of Artificial Intelligence in Health CareHowell M., Corrado G., DeSalvo K.
JAMA. 331(3):242–244 (2024).
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Reviews
Artificial intelligence in healthcare: a perspective from GoogleLehmann, L. S., Natarajan, V. & Peng, L. Chapter 39
(ed. Krittanawong, C.) Artificial Intelligence in Clinical Practice. 341–344 (Academic Press, 2024).
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Reviews
Explaining counterfactual imagesLang, O., Traynis, I. & Liu, Y.
Nat Biomed Eng (2023).[readcube]
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Reviews
Beyond Predictions: Explainability and Learning from Machine LearningDeng, C.-Y., Mitani, A., Chen, C. W., Peng, L. H., Hammel, N. & Liu, Y
(eds. Yogesan, K., Goldschmidt, L., Cuadros, J. & Ricur, G.) 199–218. Springer International Publishing, 2023.[readcube]
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Reviews
Deep Learning for Epidemiologists: An introduction to neural networks.Serghiou, S. & Rough, K.
Am. J. Epidemiol. (2023).
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Blog Posts
Building a Clinical Team in a Large Technology Company.DeSalvo Karen B. & Howell Michael D.
NEJM Catalyst non-issue commentary (2023).
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Reviews
Medicine’s Role in Reimagining Public Health: Reuniting Panacea and HygeiaDeSalvo, K. B., Kadakia, K. T. & Chokshi, D. A.
JAMA Health Forum 2, e214051–e214051 (2021).
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Reviews
Modernizing Public Health Data Systems: Lessons From the Health Information Technology for Economic and Clinical Health (HITECH) ActKadakia, K. T., Howell, M. D. & DeSalvo, K. B.
JAMA 326, 385–386 (2021).
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Reviews
Public Health 3.0 After COVID-19-Reboot or Upgrade?DeSalvo, K. B. & Kadakia, K. T.
Am. J. Public Health 111, S179–S181 (2021).
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Reviews
A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AISounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., Kahn, C. E., Jr, Esteva, A., Karthikesalingam, A., Mateen, B., Webster, D., Milea, D., Ting, D., Treanor, D., Cushnan, D., King, D., McPherson, D., Glocker, B., Greaves, F., Harling, L., Ordish, J., Cohen, J. F., Deeks, J., Leeflang, M., Diamond, M., McInnes, M. D. F., McCradden, M., Abràmoff, M. D., Normahani, P., Markar, S. R., Chang, S., Liu, X., Mallett, S., Shetty, S., Denniston, A., Collins, G. S., Moher, D., Whiting, P., Bossuyt, P. M. & Darzi, A.
Nat. Med. (2021).
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Reviews
Evaluation of artificial intelligence on a reference standard based on subjective interpretationChen, P.-H. C., Mermel, C. H. & Liu, Y.
The Lancet Digital Health (2021). doi:10.1016/S2589-7500(21)00216-8
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Reviews
Artificial Intelligence in MedicineKelly, C. J., Brown, A. P. Y. & Taylor, J. A.
(eds. Lidströmer, N. & Ashrafian, H.) 1–18 (Springer International Publishing, 2021).
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Reviews
Challenges of Accuracy in Germline Clinical Sequencing DataPoplin, R., Zook, J. M. & DePristo, M.
JAMA 326, 268–269 (2021).
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Reviews
Retinal detection of kidney disease and diabetesMitani, A., Hammel, N. & Liu, Y.
Nature Biomedical Engineering 1–3 (2021). [readcube]
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Reviews
Deep learning-enabled medical computer visionEsteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J. & Socher, R.
npj Digital Medicine 4, 5 (2021).
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Reviews
Closing the translation gap: AI applications in digital pathologySteiner, D. F., Chen, P.-H. C. & Mermel, C. H.
Biochim. Biophys. Acta Rev. Cancer 1875, 188452 (2021).
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Reviews
Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographsLiu, Y., Yang, L., Phene, S. & Peng, L.
Artificial Intelligence in Medicine 247–264 (2021).
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Reviews
Resonate: Reaching Excellence Through Equity, Diversity, and Inclusion in ISMRMWarnert, E. A. H., Kasper, L., Meltzer, C. C., Lightfoote, J. B., Bucknor, M. D., Haroon, H., Duggan, G., Gowland, P., Wald, L., Miller, K. L., Morris, E. A. & Anazodo, U. C.
J. Magn. Reson. Imaging (2020). doi:10.1002/jmri.27476 [readcube]
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Reviews
Current and future applications of artificial intelligence in pathology: a clinical perspectiveRakha, E. A., Toss, M., Shiino, S., Gamble, P., Jaroensri, R., Mermel, C. H. & Chen, P.-H. C.
J. Clin. Pathol. (2020). doi:10.1136/jclinpath-2020-206908
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Reviews
Artificial intelligence, machine learning and deep learning for eye care specialistsSayres, R., Hammel, N. & Liu, Y.
Annals of Eye Science 5, 18–18 (2020).
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Reviews
Artificial intelligence in digital breast pathology: Techniques and applicationsIbrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C. & Rakha, E. A.
Breast 49, 267–273 (2020).
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Reviews
How to Read Articles That Use Machine Learning: Users’ Guides to the Medical LiteratureLiu, Y., Chen, P.-H. C., Krause, J. & Peng, L.
JAMA 322, 1806–1816 (2019). [readcube]
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Reviews
Key challenges for delivering clinical impact with artificial intelligenceKelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.
BMC Med. 17, 195 (2019).
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Reviews
Ensuring Fairness in Machine Learning to Advance Health EquityRajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H.
Ann. Intern. Med. 169(12):866-872 (2018).
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Reviews
Artificial Intelligence Approach in MelanomaCuriel-Lewandrowski, C., Novoa, R. A., Berry, E., Celebi, M. E., Codella, N., Giuste, F., Gutman, D., Halpern, A., Leachman, S., Liu, Y., Liu, Y., Reiter, O. & Tschandl, P.
599–628. Springer New York (2019).
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Reviews
How to develop machine learning models for healthcareChen, C. P.-H., Liu, Y., & Peng, L.
Nat. Mater. 18, 410–414 (2019). [readcube]
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Reviews
Machine Learning in MedicineRajkomar, A., Dean, J., & Kohane I.
N. Engl. J. Med. 380:1347-1358 (2019).
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Reviews
A guide to deep learning in healthcareEsteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S. & Dean, J.
Nat. Med. 25, 24–29 (2019). [readcube]
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Reviews
When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ dataRough K, Thompson J.
Ophthalmology. 125(8):1136-1138 (2018).
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Reviews
Resolving the Productivity Paradox of Health Information Technology: A Time for OptimismWachter, R. M., Howell, M. D.
JAMA 320(1):25-26 (2018).
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Blog Posts
Developing reliable AI tools for healthcareby Krishnamurthy (Dj) Dvijotham & Taylan Cemgil
Google DeepMind Blog | 17-Jul-2023
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Blog Posts
Robust and efficient medical imaging with self-supervisionby Shekoofeh Azizi & Laura Culp
Google Research Blog | 26-Apr-2023
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Blog Posts
How Underspecification Presents Challenges for Machine Learningby Alex D’Amour & Katherine Heller
Google Research Blog | 18-Oct-2021
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Blog Posts
Self-Supervised Learning Advances Medical Image Classificationby Shekoofeh Azizi
Google Research Blog | 13-Oct-2021
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Publications
Generative models improve fairness of medical classifiers under distribution shifts.Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A. & Gowal, S.
Nat. Med. (2024).
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Publications
Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.Martindale, A. P. L., Ng, B., Ngai, V., Kale, A. U., Ferrante di Ruffano, L., Golub, R. M., Collins, G. S., Moher, D., McCradden, M. D., Oakden-Rayner, L., Rivera, S. C., Calvert, M., Kelly, C. J., Lee, C. S., Yau, C., Chan, A.-W., Keane, P. A., Beam, A. L., Denniston, A. K. & Liu, X.
Nat. Commun. 15, 1619 (2024).
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Publications
Understanding metric-related pitfalls in image analysis validationReinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B. A., Farahani, K., Ferrer, L., Galdran, A., van Ginneken, B., Glocker, B., Godau, P., Hashimoto, D. A., Hoffman, M. M., Huisman, M., Isensee, F., Jannin, P., Kahn, C. E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B. A., Litjens, G., Madani, A., Maier-Hein, K., Martel, A. L., Meijering, E., Menze, B., Moons, K. G. M., Müller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S. M., Rajpoot, N., Reyes, M., Riegler, M. A., Rieke, N., Saez-Rodriguez, J., Sánchez, C. I., Shetty, S., Summers, R. M., Taha, A. A., Tiulpin, A., Tsaftaris, S. A., Van Calster, B., Varoquaux, G., Yaniv, Z. R., Jäger, P. F. & Maier-Hein, L.
Nat. Methods 21, 182–194 (2024).[readcube]
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Publications
Detecting shortcut learning for fair medical AI using shortcut testingBrown, A., Tomasev, N., Freyberg, J., Liu, Y., Karthikesalingam, A. & Schrouff, J.
Nat. Commun. 14, 4314 (2023).
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Publications
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to cliniciansDvijotham, K., Winkens, J., Barsbey, M., Ghaisas, S., Stanforth, R., Pawlowski, N., Strachan, P., Ahmed, Z., Azizi, S., Bachrach, Y., Culp, L., Daswani, M., Freyberg, J., Kelly, C., Kiraly, A., Kohlberger, T., McKinney, S., Mustafa, B., Natarajan, V., Geras, K., Witowski, J., Qin, Z. Z., Creswell, J., Shetty, S., Sieniek, M., Spitz, T., Corrado, G., Kohli, P., Cemgil, T. & Karthikesalingam, A.
Nat. Med. 1–7 (2023).
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Publications
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imagingAzizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., Mahdavi, S. S., Wulczyn, E., Babenko, B., Walker, M., Loh, A., Chen, P.-H. C., Liu, Y., Bavishi, P., McKinney, S. M., Winkens, J., Roy, A. G., Beaver, Z., Ryan, F., Krogue, J., Etemadi, M., Telang, U., Liu, Y., Peng, L., Corrado, G. S., Webster, D. R., Fleet, D., Hinton, G., Houlsby, N., Karthikesalingam, A., Norouzi, M. & Natarajan, V.
Nature Biomedical Engineering 1–24 (2023). [readcube]
-
Publications
Diagnosing failures of fairness transfer across distribution shift in real-world medical settingsSchrouff, J., Harris, N., Koyejo, O. O., Alabdulmohsin, I., Schnider, E., Opsahl-Ong, K., Brown, A., Roy, S., Mincu, D., Chen, C., Dieng, A., Liu, Y., Natarajan, V., Karthikesalingam, A., Heller, K. A., Chiappa, S. & D’Amour, A.
NeurIPS (2022).
-
Publications
Comparing human and AI performance in medical machine learning: An open-source Python library for the statistical analysis of reader study dataMcKinney, S. M.
medRxiv (2022).
-
Publications
Iterative Quality Control Strategies for Expert Medical Image LabelingFreeman, B., Hammel, N., Phene, S., Huang, A., Ackermann, R., Kanzheleva, O., Hutson, M., Taggart, C., Duong, Q. & Sayres, R.
HCOMP 9, 60–71 (2021).
-
Publications
Big Self-Supervised Models Advance Medical Image ClassificationAzizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 3478–3488 (2021).
-
Publications
Privacy-first health research with federated learningSadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., MacFarlane, J., Vullikanti, A., Marathe, M., Eastham, P., Brownstein, J. S., Arcas, B. A. Y., Howell, M. D. & Hernandez, J.
NPJ Digit Med 4, 132 (2021).
-
Publications
Supervised Transfer Learning at Scale for Medical ImagingMustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Karthikesalingam, A., Houlsby, N. & Natarajan, V.
arXiv [cs.CV] (2021).
-
Publications
Big Self-Supervised Models Advance Medical Image ClassificationAzizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
arXiv [eess.IV] (2021).
-
Publications
Underspecification Presents Challenges for Credibility in Modern Machine LearningD’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C., Mincu, D., Mitani, A., Montanari, A., Nado, Z., Natarajan, V., Nielson, C., Osborne, T. F., Raman, R., Ramasamy, K., Sayres, R., Schrouff, J., Seneviratne, M., Sequeira, S., Suresh, H., Veitch, V., Vladymyrov, M., Wang, X., Webster, K., Yadlowsky, S., Yun, T., Zhai, X. & Sculley, D.
arXiv [cs.LG] (2020).
-
Publications
Contrastive Training for Improved Out-of-Distribution DetectionWinkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., Cemgil, T., Ali Eslami, S. M. & Ronneberger, O.
arXiv [cs.LG] (2020).
-
Publications
Customization scenarios for de-identification of clinical notesHartman, T., Howell, M., Dean, J., Hoory, S., Slyper, R., Laish, I., Gilon, O, Vainstein, D., Corrado, G., Chou, K., Po, M., Williams, J., Ellis, S., Bee, G., Hassidim, A., Amira, R., Beryozkin, G., Szpektor, I., & Matias, Y.
BMC (2020).
-
Blog Posts
How we’re using AI to connect people to health informationGoogle Keyword Blog | 19-Mar-2024
-
Blog Posts
3 ways we are building equity into our health workby Ivor Horn
Google Keyword Blog | 19-Mar-2024
-
Blog Posts
SCIN: A new resource for representative dermatology imagesby Pooja Rao
Google Research Blog | 19-Mar-2024
-
Blog Posts
HEAL: A framework for health equity assessment of machine learning performanceby Mike Schaekermann & Ivor Horn
Google Research Blog | 15-Mar-2024
-
Blog Posts
Health-specific embedding tools for dermatology and pathologyby Dave Steiner & Rory Pilgrim
Google Research Blog | 8-Mar-2024
-
Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
Google Keyword Blog | 18-Jul-2023
-
Blog Posts
8 ways Google Lens can help make your life easierby Lou Wang
Google Keyword Blog | 14-Jun-2023
-
Blog Posts
Ask a Techspert: What does AI do when it doesn’t know?by Iz Conroy
Google Keyword Blog | 08-Feb-2022
-
Blog Posts
Does Your Medical Image Classifier Know What It Doesn’t Know?by Abhijit Guha Roy & Jie Ren
Google Research Blog | 27-Jan-2022
-
Blog Posts
How DermAssist uses TensorFlow.js for on-device image quality checksby Miles Hutson & Aaron Loh
TensorFlow Blog | 11-Oct-2021
-
Blog Posts
Using AI to help find answers to common skin conditionsby Peggy Bui & Yuan Liu
Google Keyword Blog | 18-May-2021
-
Blog Posts
AI assists doctors in interpreting skin conditionsby Ayush Jain & Peggy Bui
Google Keyword Blog | 28-Apr-2021
-
Blog Posts
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
Google Research Blog | 19-Feb-2020
-
Blog Posts
Using Deep Learning to Inform Differential Diagnoses of Skin Diseasesby Yuan Liu & Peggy Bui
Google Research Blog | 12-Sep-2019
-
Publications
Searching for dermatology information online using images vs text: A randomized studyKrogue, J. D., Sayres, R., Hartford, J., Talreja, A., Bavishi, P., Salaets, N., Raiford, K., Nayar, J., Patel, R., Matias, Y., Corrado, G. S., Berrada, D., Kharbanda, H., Wang, L., Webster, D. R., Duong, Q., Bui, P. & Liu, Y.
medRxiv (2024).
-
Publications
Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case studySchaekermann, M., Spitz, T., Pyles, M., Cole-Lewis, H., Wulczyn, E., Pfohl, S. R., Martin, D., Jr, Jaroensri, R., Keeling, G., Liu, Y., Farquhar, S., Xue, Q., Lester, J., Hughes, C., Strachan, P., Tan, F., Bui, P., Mermel, C. H., Peng, L. H., Matias, Y., Corrado, G. S., Webster, D. R., Virmani, S., Semturs, C., Liu, Y., Horn, I. & Cameron Chen, P.-H.
eClinicalMedicine (2024).
-
Blog Posts
Differences Between Patient and Clinician-Taken Images: Implications for Virtual Care of Skin ConditionsRikhye, R. V., Hong, G. E., Singh, P., Smith, M. A., Loh, A., Muralidharan, V., Wong, D., Sayres, R., Phung, M., Betancourt, N., Fong, B., Sahasrabudhe, R., Nasim, K., Eschholz, A., Matias, Y., Corrado, G. S., Chou, K., Webster, D. R., Bui, P., Liu, Y., Liu, Y., Ko, J. & Lin, S.
Mayo Clinic Proceedings: Digital Health (2024).
-
Publications
Conformal prediction under ambiguous ground truthStutz, D., Roy, A. G., Matejovicova, T., Strachan, P., Cemgil, A. T. & Doucet, A.
Transactions on Machine Learning Research (2023).
-
Publications
A Reduction to Binary Approach for Debiasing Multiclass Datasets. Advances in Neural Information Processing SystemsAlabdulmohsin, I.M., Schrouff, J., Koyejo, S.
35. NeurIPS (2022).
-
Publications
Federated Training of Dual Encoding Models on Small Non-IID Client DatasetsVemulapalli, R., Morningstar, W. R., Mansfield, P. A., Eichner, H., Singhal, K., Afkanpour, A. & Green, B.
arXiv [cs.LG] (2022).
-
Publications
Machine learning for clinical operations improvement via case triagingHuang, S. J., Liu, Y., Kanada, K., Corrado, G. S., Webster, D. R., Peng, L., Bui, P. & Liu, Y.
Skin Health and Disease (2021).
-
Publications
Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditionsGuha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., Vo, N., Bui, P., Winter, S., MacWilliams, P., Corrado, G. S., Telang, U., Liu, Y., Cemgil, T., Karthikesalingam, A., Lakshminarayanan, B. & Winkens, J.
Med. Image Analysis. 75, 102274 (2021). [reading link]
-
Publications
Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology PracticesWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.
JAMA Netw Open 4, e217249–e217249 (2021).
-
Publications
Addressing the Real-world Class Imbalance Problem in DermatologyWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.
Machine Learning for Health NeurIPS Workshop (ML4H), PMLR 136:415-429 (2020).
-
Publications
Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease ClassificationSingh, N., Lee, K., Coz, D., Angermueller, C., Huang, S., Loh, A. & Liu, Y.
in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 3172–3181 (2020).
-
Publications
A deep learning system for differential diagnosis of skin diseasesLiu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., Gupta, V., Singh, N., Natarajan, V., Hofmann-Wellenhof, R., Corrado, G. S., Peng, L. H., Webster, D. R., Ai, D., Huang, S., Liu, Y., Carter Dunn, R. & Coz, D.
Nat. Med. (2020). [readcube]
-
Publications
DermGAN: Synthetic Generation of Clinical Skin Images with PathologyGhorbani, A., Natarajan, V., Coz, D. & Liu, Y.
Machine Learning for Health NeurIPS Workshop (ML4H), PMLR 116:155-170 (2020).
-
Publications
Measuring clinician-machine agreement in differential diagnoses for dermatologyEng, C., Liu, Y. & Bhatnagar, R.
Br. J. Dermatol. (2019). readcube
-
Blog Posts
Improved Detection of Elusive Polyps via Machine Learningby Yossi Matias & Ehud Rivlin
Google Research Blog | 5-Aug-2021
-
Blog Posts
Verily Opens New R&D Center in Israel Focused on the Application of AI in Healthcareby Robin Suchan
Verily Press | 5-Aug-2021
-
Blog Posts
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screeningsby Daniel Freedman & Ehud Rivlin
Google Research Blog | 28-Aug-2020
-
Publications
Artificial intelligence for phase recognition in complex laparoscopic cholecystectomyGolany, T., Aides, A., Freedman, D., Rabani, N., Liu, Y., Rivlin, E., Corrado, G. S., Matias, Y., Khoury, W., Kashtan, H. & Reissman, P.
Surg. Endosc. (2022).
-
Publications
Detection of elusive polyps via a large-scale artificial intelligence system (with videos)Livovsky, D. M., Veikherman, D., Golany, T., Aides, A., Dashinsky, V., Rabani, N., Ben Shimol, D., Blau, Y., Katzir, L., Shimshoni, I., Liu, Y., Segol, O., Goldin, E., Corrado, G., Lachter, J., Matias, Y., Rivlin, E. & Freedman, D.
Gastrointest. Endosc. (2021).
-
Publications
Detecting Deficient Coverage in ColonoscopiesFreedman, D., Blau, Y., Katzir, L., Aides, A., Shimshoni, I., Veikherman, D., Golany, T., Gordon, A., Corrado, G., Matias, Y. & Rivlin, E.
IEEE Trans. Med. Imaging 1–1 (2020).
-
Blog Posts
An ML-Based Framework for COVID-19 Epidemiologyby Joel Shor & Sercan Arik
Google Research Blog | 13-Oct-2021
-
Blog Posts
Google Cloud, Harvard Global Health Institute release improved COVID-19 Public Forecasts, share lessons learnedby Tomas Pfister
Google Cloud Blog | 15-Nov-2020
-
Blog Posts
Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID-19 forecasting modelby Dario Sava
Google Cloud Blog | 3-Aug-2020
-
Publications
Algorithmic fairness in pandemic forecasting: lessons from COVID-19Tsai, T. C., Arik, S., Jacobson, B. H., Yoon, J., Yoder, N., Sava, D., Mitchell, M., Graham, G. & Pfister, T.
NPJ Digit Med 5, 59 (2022).
-
Publications
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United StatesCramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., Jayawardena, D., Kanji, A. H., Khandelwal, A., Le, K., Mühlemann, A., Niemi, J., Shah, A., Stark, A., Wang, Y., Wattanachit, N., Zorn, M. W., Gu, Y., Jain, S., Bannur, N., Deva, A., Kulkarni, M., Merugu, S., Raval, A., Shingi, S., Tiwari, A., White, J., Abernethy, N. F., Woody, S., Dahan, M., Fox, S., Gaither, K., Lachmann, M., Meyers, L. A., Scott, J. G., Tec, M., Srivastava, A., George, G. E., Cegan, J. C., Dettwiller, I. D., England, W. P., Farthing, M. W., Hunter, R. H., Lafferty, B., Linkov, I., Mayo, M. L., Parno, M. D., Rowland, M. A., Trump, B. D., Zhang-James, Y., Chen, S., Faraone, S. V., Hess, J., Morley, C. P., Salekin, A., Wang, D., Corsetti, S. M., Baer, T. M., Eisenberg, M. C., Falb, K., Huang, Y., Martin, E. T., McCauley, E., Myers, R. L., Schwarz, T., Sheldon, D., Gibson, G. C., Yu, R., Gao, L., Ma, Y., Wu, D., Yan, X., Jin, X., Wang, Y.-X., Chen, Y., Guo, L., Zhao, Y., Gu, Q., Chen, J., Wang, L., Xu, P., Zhang, W., Zou, D., Biegel, H., Lega, J., McConnell, S., Nagraj, V. P., Guertin, S. L., Hulme-Lowe, C., Turner, S. D., Shi, Y., Ban, X., Walraven, R., Hong, Q.-J., Kong, S., van de Walle, A., Turtle, J. A., Ben-Nun, M., Riley, S., Riley, P., Koyluoglu, U., DesRoches, D., Forli, P., Hamory, B., Kyriakides, C., Leis, H., Milliken, J., Moloney, M., Morgan, J., Nirgudkar, N., Ozcan, G., Piwonka, N., Ravi, M., Schrader, C., Shakhnovich, E., Siegel, D., Spatz, R., Stiefeling, C., Wilkinson, B., Wong, A., Cavany, S., España, G., Moore, S., Oidtman, R., Perkins, A., Kraus, D., Kraus, A., Gao, Z., Bian, J., Cao, W., Lavista Ferres, J., Li, C., Liu, T.-Y., Xie, X., Zhang, S., Zheng, S., Vespignani, A., Chinazzi, M., Davis, J. T., Mu, K., Pastore Y Piontti, A., Xiong, X., Zheng, A., Baek, J., Farias, V., Georgescu, A., Levi, R., Sinha, D., Wilde, J., Perakis, G., Bennouna, M. A., Nze-Ndong, D., Singhvi, D., Spantidakis, I., Thayaparan, L., Tsiourvas, A., Sarker, A., Jadbabaie, A., Shah, D., Della Penna, N., Celi, L. A., Sundar, S., Wolfinger, R., Osthus, D., Castro, L., Fairchild, G., Michaud, I., Karlen, D., Kinsey, M., Mullany, L. C., Rainwater-Lovett, K., Shin, L., Tallaksen, K., Wilson, S., Lee, E. C., Dent, J., Grantz, K. H., Hill, A. L., Kaminsky, J., Kaminsky, K., Keegan, L. T., Lauer, S. A., Lemaitre, J. C., Lessler, J., Meredith, H. R., Perez-Saez, J., Shah, S., Smith, C. P., Truelove, S. A., Wills, J., Marshall, M., Gardner, L., Nixon, K., Burant, J. C., Wang, L., Gao, L., Gu, Z., Kim, M., Li, X., Wang, G., Wang, Y., Yu, S., Reiner, R. C., Barber, R., Gakidou, E., Hay, S. I., Lim, S., Murray, C., Pigott, D., Gurung, H. L., Baccam, P., Stage, S. A., Suchoski, B. T., Prakash, B. A., Adhikari, B., Cui, J., Rodríguez, A., Tabassum, A., Xie, J., Keskinocak, P., Asplund, J., Baxter, A., Oruc, B. E., Serban, N., Arik, S. O., Dusenberry, M., Epshteyn, A., Kanal, E., Le, L. T., Li, C.-L., Pfister, T., Sava, D., Sinha, R., Tsai, T., Yoder, N., Yoon, J., Zhang, L., Abbott, S., Bosse, N. I., Funk, S., Hellewell, J., Meakin, S. R., Sherratt, K., Zhou, M., Kalantari, R., Yamana, T. K., Pei, S., Shaman, J., Li, M. L., Bertsimas, D., Skali Lami, O., Soni, S., Tazi Bouardi, H., Ayer, T., Adee, M., Chhatwal, J., Dalgic, O. O., Ladd, M. A., Linas, B. P., Mueller, P., Xiao, J., Wang, Y., Wang, Q., Xie, S., Zeng, D., Green, A., Bien, J., Brooks, L., Hu, A. J., Jahja, M., McDonald, D., Narasimhan, B., Politsch, C., Rajanala, S., Rumack, A., Simon, N., Tibshirani, R. J., Tibshirani, R., Ventura, V., Wasserman, L., O’Dea, E. B., Drake, J. M., Pagano, R., Tran, Q. T., Ho, L. S. T., Huynh, H., Walker, J. W., Slayton, R. B., Johansson, M. A., Biggerstaff, M. & Reich, N. G.
Proc. Natl. Acad. Sci. U. S. A. 119, e2113561119 (2022).
-
Publications
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and JapanArık, S. Ö., Shor, J., Sinha, R., Yoon, J., Ledsam, J. R., Le, L. T., Dusenberry, M. W., Yoder, N. C., Popendorf, K., Epshteyn, A., Euphrosine, J., Kanal, E., Jones, I., Li, C.-L., Luan, B., Mckenna, J., Menon, V., Singh, S., Sun, M., Ravi, A. S., Zhang, L., Sava, D., Cunningham, K., Kayama, H., Tsai, T., Yoneoka, D., Nomura, S., Miyata, H. & Pfister, T.
NPJ Digit Med 4, 146 (2021).
-
Publications
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksKapoor, A., Ben, X., Liu, L., Perozzi, B., Barnes, M., Blais, M. & O’Banion, S.
arXiv [cs.LG] (2020).
-
Publications
Interpretable Sequence Learning for Covid-19 ForecastingArik, Li, Yoon, Sinha, Epshteyn, Le, Menon, Singh, Zhang, Nikoltchev, Sonthalia, Nakhost, Kanal & Pfister.
Adv. Neural Inf. Process. Syst. 2020.
-
Blog Posts
How AI is making eyesight-saving care more accessible in resource-constrained settingsby Rajroshan Sawhney
Google Keyword Blog | 17-Oct-2024
-
Blog Posts
Supporting a healthier and greener India with our AIby the Google India Team
Google India Blog | 17-Oct-2024
-
Blog Posts
Google at 25: By the numbersby Michelle Budzyna & Molly McHugh-Johnson
Google Keyword Blog | 27-Sep-2023
-
Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
Google Keyword Blog | 18-Jul-2023
-
Blog Posts
5 myths about medical AI, debunkedby Kasumi Widner
Google Keyword Blog | 30-May-2023
-
Blog Posts
An eye to the future: How AI could help to improve detection of eye disease in Australian communitiesby Angus Turner
Google Australia Blog | 7-Mar-2023
-
Blog Posts
Healthcare AI systems that put people at the centerby Emma Beede
Google Keyword Blog | 25-Apr-2020
-
Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
Google Research Blog | 24-Mar-2022
-
Blog Posts
New milestones in helping prevent eye disease with Verilyby Kasumi Widner & Sunny Virmani
Google Keyword Blog | 25-Feb-2019
-
Blog Posts
Launching a powerful new screening tool for diabetic eye disease in IndiaVerily Blog | 25-Feb-2019
-
Blog Posts
AI for Social Good in Asia Pacificby Kent Walter
Google Keyword Blog | 13-Dec-2018
-
Blog Posts
Improving the Effectiveness of Diabetic Retinopathy Modelsby Rory Sayres & Jonathan Krause
Google Research Blog | 13-Dec-2018
-
Blog Posts
A major milestone for the treatment of eye diseaseby Mustafa Suleyman
DeepMind Blog | 13-Aug-2018
-
Blog Posts
Detecting diabetic eye disease with machine learningby Lily Peng
Google Keyword Blog | 29-Nov-2016
-
Blog Posts
Deep learning for Detection of Diabetic Eye Diseaseby Lily Peng & Varun Gulshan
Google Research Blog | 29-Nov-2016
-
Publications
Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective StudyBora, A., Tiwari, R., Bavishi, P., Virmani, S., Huang, R., Traynis, I., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A. V., Pattanapongpaiboon, W., Chopra, R. & Ruamviboonsuk, P.
Transl. Vis. Sci. Technol. 12, 11 (2023).
-
Publications
Lessons learned from translating AI from development to deployment in healthcareWidner, K., Virmani, S., Krause, J., Nayar, J., Tiwari, R., Pedersen, E. R., Jeji, D., Hammel, N., Matias, Y., Corrado, G. S., Liu, Y., Peng, L. & Webster, D. R.
Nat. Med. 1–3 (2023). [readcube]
-
Publications
Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide ProgramSrisubat, A., Kittrongsiri, K., Sangroongruangsri, S., Khemvaranan, C., Shreibati, J. B., Ching, J., Hernandez, J., Tiwari, R., Hersch, F., Liu, Y., Hanutsaha, P., Ruamviboonsuk, V., Turongkaravee, S., Raman, R. & Ruamviboonsuk, P.
Ophthalmol Ther (2023).
-
Publications
Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous AustraliansChia, M. A., Hersch, F., Sayres, R., Bavishi, P., Tiwari, R., Keane, P. A. & Turner, A. W.
Br. J. Ophthalmol. (2023).
-
Publications
Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort studyRuamviboonsuk, P., Tiwari, R., Sayres, R., Nganthavee, V., Hemarat, K., Kongprayoon, A., Raman, R., Levinstein, B., Liu, Y., Schaekermann, M., Lee, R., Virmani, S., Widner, K., Chambers, J., Hersch, F., Peng, L. & Webster, D. R.
The Lancet Digital Health (2022).
-
Publications
Redesigning Clinical Pathways for Immediate Diabetic Retinopathy Screening ResultsPedersen Elin Rønby, Cuadros Jorge, Khan Mahbuba, Fleischmann Sybille, Wolff Gregory, Hammel Naama, Liu Yun & Leung Geoffrey.
NEJM Catalyst (2021).
-
Publications
Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep LearningWilson, M., Chopra, R., Wilson, M. Z., Cooper, C., MacWilliams, P., Liu, Y., Wulczyn, E., Florea, D., Hughes, C. O., Karthikesalingam, A., Khalid, H., Vermeirsch, S., Nicholson, L., Keane, P. A., Balaskas, K. & Kelly, C. J.
JAMA Ophthalmol. (2021).
-
Publications
Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human GradersLimwattanayingyong, J., Nganthavee, V., Seresirikachorn, K., Singalavanija, T., Soonthornworasiri, N., Ruamviboonsuk, V., Rao, C., Raman, R., Grzybowski, A., Schaekermann, M., Peng, L. H., Webster, D. R., Semturs, C., Krause, J., Sayres, R., Hersch, F., Tiwari, R., Liu, Y. & Ruamviboonsuk, P.
Journal of Diabetes Research, 1–8 (2020).
-
Publications
Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validationHsu J, Phene S, Mitani A, Luo J, Hammel N, Krause J, Sayres R.
in ACM-CHIL [arXiv](2020)
-
Publications
Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care settingBresnick, G., Cuadros, J. A., Khan, M., Fleischmann, S., Wolff, G., Limon, A., Chang, J., Jiang, L., Cuadros, P. & Pedersen, E. R.
BMJ Open Diabetes Research and Care 8, e001154 (2020).
-
Publications
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyBeede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P. & Vardoulakis, L. M.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12. Association for Computing Machinery (2020).
-
Publications
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image AssessmentSchaekermann, M., Cai, C. J., Huang, A. E. & Sayres, R.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–13. Association for Computing Machinery (2020).
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Publications
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus PhotographsPhene, S., Dunn, R. C., Hammel, N., Liu, Y., Krause, J., Kitade, N., Schaekermann, M., Sayres, R., Wu, D. J., Bora, A., Semturs, C., Misra, A., Huang, A. E., Spitze, A., Medeiros, F. A., Maa, A. Y., Gandhi, M., Corrado, G. S., Peng, L. & Webster, D. R.
Ophthalmology 126, 1627–1639 (2019).
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Publications
Remote Tool-Based Adjudication for Grading Diabetic RetinopathySchaekermann, M., Hammel, N., Terry, M., Ali, T. K., Liu, Y., Basham, B., Campana, B., Chen, W., Ji, X., Krause, J., Corrado, G. S., Peng, L., Webster, D. R., Law, E. & Sayres, R.
Transl. Vis. Sci. Technol. 8, 40 (2019).
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Publications
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in IndiaGulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., Raman, R., Peng, L. & Webster, D. R.
JAMA Ophthalmol. (2019).
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Publications
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening programRuamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., Silpa-Archa, S., Limwattanayingyong, J., Rao, C., Kuruvilla, O., Jung, J., Tan, J., Orprayoon, S., Kangwanwongpaisan, C., Sukumalpaiboon, R., Luengchaichawang, C., Fuangkaew, J., Kongsap, P., Chualinpha, L., Saree, S., Kawinpanitan, S., Mitvongsa, K., Lawanasakol, S., Thepchatri, C., Wongpichedchai, L., Corrado, G. S., Peng, L. & Webster, D. R.
npj Digit Med 2, 25 (2019).
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Publications
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic RetinopathySayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., Xu, S., Barb, S., Joseph, A., Shumski, M., Smith, J., Sood, A. B., Corrado, G. S., Peng, L. & Webster, D. R.
Ophthalmology 126, 552–564 (2019).
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Publications
Clinically applicable deep learning for diagnosis and referral in retinal diseaseFauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., van den Driessche, G., Lakshminarayanan, B., Meyer, C., Mackinder, F., Bouton, S., Ayoub, K., Chopra, R., King, D., Karthikesalingam, A., Hughes, C.O., Raine, R., Hughes, J., Sim, D. A., Egan, C., Tufail, A., Montgomery, H., Hassabis, D., Rees, G., Back, T., Khaw, P.T., Suleyman, M., Cornebise, J., Keane, P.A., & Ronneberger, O.
Nat. Med. 24, 1342–1350 (2018). [readcube]
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Publications
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic RetinopathyKrause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D.R.
Ophthalmology 125, 1264–1272 (2018).[arXiv]
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Publications
Blind spots in telemedicine: a qualitative study of staff workarounds to resolve gaps in diabetes managementBouskill, K., Smith-Morris, C., Bresnick, G., Cuadros, J. & Pedersen, E. R.
BMC Health Services Research 18, (2018).
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Publications
Diabetic Retinopathy and the Cascade into Vision LossSmith-Morris, C., Bresnick, G. H., Cuadros, J., Bouskill, K. E. & Pedersen, E. R.
Med. Anthropol. 39, 109–122 (2018).
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Publications
Who Said What: Modeling Individual Labelers Improves ClassificationGuan, M., Gulshan, V., Dai, A, Hinton, G.
AAAI Conference on Artificial Intelligence (2018).
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Publications
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsGulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Ramasamy, K., Nelson, P., Mega, J., & Webster, D.
JAMA 316, 2402–2410 (2016).
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Blog Posts [more at Google Keyword Blog]
Loss of Pulse Detection: A first-of-its-kind feature on Pixel Watch 3by Tajinder Gadh & Pramod Rudrapatna
Google Keyword Blog | 13-Aug-2024
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A new study using Fitbit data uncovers connections between sleep and diseaseby Logan Schneider & Evan Brittain
Google Keyword Blog | 24-Jul-2024
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Health partners can now more easily access Fitbit heart databy Kapil Parakh
Google Keyword Blog | 10-Jul-2024
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How Fitbit can help you measure stress — and use it to your advantageby Molly McHugh-Johnson
Google Keyword Blog | 17-Apr-2024
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How we’re using AI to connect people to health informationby Karen DeSalvo
Google Keyword Blog | 19-Mar-2024
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3 heart-health tips from Fitbit’s lead cardiologistby Molly McHugh-Johnson
Google Keyword Blog | 6-Mar-2024
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6 things I learned after using the Fitbit Charge 6 for a weekby Mike Darling
Google Keyword Blog | 24-Jan-2024
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New Fitbit study explores metabolic healthby Javier L. Prieto
Google Keyword Blog | 17-Jan-2024
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3 ways Fitbit can improve your health — backed by researchby Amy McDonough
Google Keyword Blog | 26-Oct-2023
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How Google Pixel Watch 2 and Fitbit Charge 6 improved heart rate trackingby Molly McHugh-Johnson
Google Keyword Blog | 19-Oct-2023
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Google Pixel Watch 2: New ways to stay healthy, connected and safeby Sandeep Waraich
Google Keyword Blog | 4-Oct-2023
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Introducing Fitbit Charge 6: Our most advanced tracker yetby TJ Varghese
Google Keyword Blog | 28-Sep-2023
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Meet the new Fitbit app that’s redesigned with you in mindby Maggie Stanphill & Bhanu Narasimhan
Google Keyword Blog | 1-Aug-2023
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How we trained Fitbit’s Body Response feature to detect stressby Elena Perez & Samy Abdel-Ghaffer
Google Keyword Blog | 2-Jun-2023
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7 ways to stress less with Fitbitby Elena Perez
Google Keyword Blog | 4-May-2023
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3 ways Google products can help you feel less stressedby Megan Jones Bell
Google Keyword Blog | 13-Apr-2023
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6 ways Google AI is helping you sleep betterby Molly McHugh-Johnson
Google Keyword Blog | 16-Mar-2023
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3 ways to take better care of your mind and body in 2023by Megan Jones Bell
Google Keyword Blog | 5-Jan-2023
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8 things we launched in 2022 to support your healthby Iz Conroy
Google Keyword Blog | 21-Dec-2022
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I tried Fitbit’s new sleep features for two monthsby Zahra Barnes
Google Keyword Blog | 20-Dec-2022
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Google Pixel Watch: Help by Google, health by Fitbitby Sandeep Waraich
Google Keyword Blog | 6-Oct-2022
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8 things to try now on Fitbit Sense 2 and Versa 4by TJ Varghese
Google Keyword Blog | 29-Sep-2022
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Our work toward health equityby Ivor Horn
Google Keyword Blog | 12-Sep-2022
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Fitbit’s fall lineup: helping you live your healthiest lifeby TJ Varghese
Google Keyword Blog | 24-Aug-2022
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Kick-start your fitness routine with Fitbit Inspire 3by The Fitbit Team
Google Keyword Blog | 24-Aug-2022
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Manage your health and fitness with Fitbit Versa 4 and Sense 2by The Fitbit Team
Google Keyword Blog | 24-Aug-2022
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Improve your ZZZs with Fitbit Premium Sleep Profileby The Fitbit Team
Google Keyword Blog | 22-Jun-2022
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Mental health resources you can count onby Megan Jones Bell
Google Keyword Blog | 17-May-2022
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New Fitbit feature makes AFib detection more accessibleby The Fitbit Team
Google Keyword Blog | 11-Apr-2022
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Blog Posts
The Check Up: helping people live healthier livesby Karen DeSalvo
Google Keyword Blog | 24-Mar-2022
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Publications
Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research ProgramZheng, N. S., Annis, J., Master, H., Han, L., Gleichauf, K., Ching, J. H., Nasser, M., Coleman, P., Desine, S., Ruderfer, D. M., Hernandez, J., Schneider, L. D. & Brittain, E. L.
Nat. Med. (2024).
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Publications
Measure by measure: Resting heart rate across the 24-hour cycleSpeed, C., Arneil, T., Harle, R., Wilson, A., Karthikesalingam, A., McConnell, M. & Phillips, J.
PLOS Digit Health 2, e0000236 (2023).
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Publications
Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart StudyLubitz, S. A., Faranesh, A. Z., Selvaggi, C., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A. & Foulkes, A. S.
Circulation (2022).
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Publications
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19Natarajan, A., Su, H.-W. & Heneghan, C.
Front. Physiol. 13, 898251 (2022).
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Publications
Measurement of respiratory rate using wearable devices and applications to COVID-19 detectionNatarajan, A., Su, H.-W., Heneghan, C., Blunt, L., O’Connor, C. & Niehaus, L.
NPJ Digit Med 4, 136 (2021).
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Blog Posts [more at DeepVariant Blog]
Learning DeepVariant's hidden powersby Atilla Kiraly & Yuchen Zhou
Google Research Blog | 22-Oct-2024
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Blog Posts [more at DeepVariant Blog]
A breakthrough to better represent human genetic diversityby Andrew Carroll
Google Keyword Blog | 10-May-2023
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Blog Posts
Building better pangenomes to improve the equity of genomicsby Andrew Carroll & Kishwar Shafin
Google Research Blog | 10-May-2023
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Blog Posts
An ML-based approach to better characterize lung diseasesBabak Behsaz & Andrew Carroll
Google Research Blog | 27-Apr-2023
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Blog Posts
Developing an aging clock using deep learning on retinal imagesby Sara Ahadi & Andrew Carroll
Google Research Blog | 11-Apr-2023
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
Google Keyword Blog | 17-Jan-2023
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Blog Posts
A new genome sequencing tool powered with our technologyby Andrew Carroll
Google Keyword Blog | 26-Oct-2022
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Blog Posts
Advancing genomics to better understand and treat diseaseby Andrew Carroll & Pi-Chuan Chang
Google Keyword Blog | 13-Jan-2022
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Blog Posts
DeepNull: an open-source method to improve the discovery power of genetic association studiesby Farhad Hormozdiari & Andrew Carroll
Google Open Source Blog | 11-Jan-2022
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Blog Posts
Improving Genomic Discovery with Machine Learningby Andrew Carroll & Cory McLean
Google Research Blog | 23-Jun-2021
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Blog Posts
Improving the Accuracy of Genomic Analysis with DeepVariant 1.0by Andrew Carroll & Pi-Chuan Chang
Google Research Blog | 18-Sep-2020
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Blog Posts
DeepVariant Accuracy Improvements for Genetic Datatypesby Pi-Chuan Chang & Lizzie Dorfman
Google Research Blog | 19-Apr-2018
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Blog Posts
DeepVariant: Highly Accurate Genomes With Deep Neural Networksby Mark DePristo & Ryan Poplin
Google Research Blog | 4-Dec-2017
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Blog Posts
An AI Resident at work: Suhani Vora and her work on genomicsby Phing Lee
Google Keyword Blog | 17-Nov-2017
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Publications
Personalized pangenome referencesSirén, J., Eskandar, P., Ungaro, M. T., Hickey, G., Eizenga, J. M., Novak, A. M., Chang, X., Chang, P.-C., Kolmogorov, M., Carroll, A., Monlong, J. & Paten, B.
Nat. Methods 1–7 (2024).
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Publications
Local read haplotagging enables accurate long-read small variant callingKolesnikov, A., Cook, D., Nattestad, M. et al.
Nat Commun 15, 5907 (2024).
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Publications
Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and predictionYun, T., Cosentino, J., Behsaz, B., McCaw, Z. R., Hill, D., Luben, R., Lai, D., Bates, J., Yang, H., Schwantes-An, T.-H., Zhou, Y., Khawaja, A. P., Carroll, A., Hobbs, B. D., Cho, M. H., McLean, C. Y. & Hormozdiari, F.
Nat. Genet. (2024).
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Publications
Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylationKolmogorov, M., Billingsley, K. J., Mastoras, M., Meredith, M., Monlong, J., Lorig-Roach, R., Asri, M., Alvarez Jerez, P., Malik, L., Dewan, R., Reed, X., Genner, R. M., Daida, K., Behera, S., Shafin, K., Pesout, T., Prabakaran, J., Carnevali, P., Yang, J., Rhie, A., Scholz, S. W., Traynor, B. J., Miga, K. H., Jain, M., Timp, W., Phillippy, A. M., Chaisson, M., Sedlazeck, F. J., Blauwendraat, C. & Paten, B.
Nat. Methods 20, 1483–1492 (2023).
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Publications
The complete sequence of a human Y chromosomeRhie, A., Nurk, S., Cechova, M., Hoyt, S. J., Taylor, D. J., Altemose, N., Hook, P. W., Koren, S., Rautiainen, M., Alexandrov, I. A., Allen, J., Asri, M., Bzikadze, A. V., Chen, N.-C., Chin, C.-S., Diekhans, M., Flicek, P., Formenti, G., Fungtammasan, A., Garcia Giron, C., Garrison, E., Gershman, A., Gerton, J. L., Grady, P. G. S., Guarracino, A., Haggerty, L., Halabian, R., Hansen, N. F., Harris, R., Hartley, G. A., Harvey, W. T., Haukness, M., Heinz, J., Hourlier, T., Hubley, R. M., Hunt, S. E., Hwang, S., Jain, M., Kesharwani, R. K., Lewis, A. P., Li, H., Logsdon, G. A., Lucas, J. K., Makalowski, W., Markovic, C., Martin, F. J., Mc Cartney, A. M., McCoy, R. C., McDaniel, J., McNulty, B. M., Medvedev, P., Mikheenko, A., Munson, K. M., Murphy, T. D., Olsen, H. E., Olson, N. D., Paulin, L. F., Porubsky, D., Potapova, T., Ryabov, F., Salzberg, S. L., Sauria, M. E. G., Sedlazeck, F. J., Shafin, K., Shepelev, V. A., Shumate, A., Storer, J. M., Surapaneni, L., Taravella Oill, A. M., Thibaud-Nissen, F., Timp, W., Tomaszkiewicz, M., Vollger, M. R., Walenz, B. P., Watwood, A. C., Weissensteiner, M. H., Wenger, A. M., Wilson, M. A., Zarate, S., Zhu, Y., Zook, J. M., Eichler, E. E., O’Neill, R. J., Schatz, M. C., Miga, K. H., Makova, K. D. & Phillippy, A. M.
Nature (2023).
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Publications
A draft human pangenome referenceLiao, W.-W., Asri, M., Ebler, J., Doerr, D., Haukness, M., Hickey, G., Lu, S., Lucas, J. K., Monlong, J., Abel, H. J., Buonaiuto, S., Chang, X. H., Cheng, H., Chu, J., Colonna, V., Eizenga, J. M., Feng, X., Fischer, C., Fulton, R. S., Garg, S., Groza, C., Guarracino, A., Harvey, W. T., Heumos, S., Howe, K., Jain, M., Lu, T.-Y., Markello, C., Martin, F. J., Mitchell, M. W., Munson, K. M., Mwaniki, M. N., Novak, A. M., Olsen, H. E., Pesout, T., Porubsky, D., Prins, P., Sibbesen, J. A., Sirén, J., Tomlinson, C., Villani, F., Vollger, M. R., Antonacci-Fulton, L. L., Baid, G., Baker, C. A., Belyaeva, A., Billis, K., Carroll, A., Chang, P.-C., Cody, S., Cook, D. E., Cook-Deegan, R. M., Cornejo, O. E., Diekhans, M., Ebert, P., Fairley, S., Fedrigo, O., Felsenfeld, A. L., Formenti, G., Frankish, A., Gao, Y., Garrison, N. A., Giron, C. G., Green, R. E., Haggerty, L., Hoekzema, K., Hourlier, T., Ji, H. P., Kenny, E. E., Koenig, B. A., Kolesnikov, A., Korbel, J. O., Kordosky, J., Koren, S., Lee, H., Lewis, A. P., Magalhães, H., Marco-Sola, S., Marijon, P., McCartney, A., McDaniel, J., Mountcastle, J., Nattestad, M., Nurk, S., Olson, N. D., Popejoy, A. B., Puiu, D., Rautiainen, M., Regier, A. A., Rhie, A., Sacco, S., Sanders, A. D., Schneider, V. A., Schultz, B. I., Shafin, K., Smith, M. W., Sofia, H. J., Abou Tayoun, A. N., Thibaud-Nissen, F., Tricomi, F. F., Wagner, J., Walenz, B., Wood, J. M. D., Zimin, A. V., Bourque, G., Chaisson, M. J. P., Flicek, P., Phillippy, A. M., Zook, J. M., Eichler, E. E., Haussler, D., Wang, T., Jarvis, E. D., Miga, K. H., Garrison, E., Marschall, T., Hall, I. M., Li, H. & Paten, B.
Nature 617, 312–324 (2023).
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Publications
Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk modelsCosentino, J., Behsaz, B., Alipanahi, B., McCaw, Z. R., Hill, D., Schwantes-An, T.-H., Lai, D., Carroll, A., Hobbs, B. D., Cho, M. H., McLean, C. Y. & Hormozdiari, F.
Nat. Genet. (2023).
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Publications
Best: A Tool for Characterizing Sequencing ErrorsLiu, D., Belyaeva, A., Shafin, K., Chang, P.-C., Carroll, A. & Cook, D. E.
bioRxiv (2022).
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Publications
Knowledge distillation for fast and accurate DNA sequence correction.Belyaeva, A., Shor, J., Cook, D. E., Shafin, K., Liu, D., Töpfer, A., Wenger, A. M., Rowell, W. J., Yang, H., Kolesnikov, A., McLean, C. Y., Nattestad, M., Carroll, A. & Chang, P.-C.
NeurIPS (2022).
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Publications
An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic DiscoveryYuan, B., McLean, C. Y., Hormozdiari, F. I. & Cosentino, J.
NeurIPS (2022).
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Publications
DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformerBaid, G., Cook, D. E., Shafin, K., Yun, T., Llinares-López, F., Berthet, Q., Belyaeva, A., Töpfer, A., Wenger, A. M., Rowell, W. J., Yang, H., Kolesnikov, A., Ammar, W., Vert, J.-P., Vaswani, A., McLean, C. Y., Nattestad, M., Chang, P.-C. & Carroll, A.
Nat. Biotechnol. (2022).
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Publications
Benchmarking challenging small variants with linked and long readsWagner, J., Olson, N. D., Harris, L., Khan, Z., Farek, J., Mahmoud, M., Stankovic, A., Kovacevic, V., Yoo, B., Miller, N., Rosenfeld, J. A., Ni, B., Zarate, S., Kirsche, M., Aganezov, S., Schatz, M. C., Narzisi, G., Byrska-Bishop, M., Clarke, W., Evani, U. S., Markello, C., Shafin, K., Zhou, X., Sidow, A., Bansal, V., Ebert, P., Marschall, T., Lansdorp, P., Hanlon, V., Mattsson, C.-A., Barrio, A. M., Fiddes, I. T., Xiao, C., Fungtammasan, A., Chin, C.-S., Wenger, A. M., Rowell, W. J., Sedlazeck, F. J., Carroll, A., Salit, M. & Zook, J. M.
Cell Genom 2, (2022).
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Publications
A complete pedigree-based graph workflow for rare candidate variant analysisMarkello, C., Huang, C., Rodriguez, A., Carroll, A., Chang, P.-C., Eizenga, J., Markello, T., Haussler, D. & Paten, B.
Genome Res. 32, 893–903 (2022).
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Publications
Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencingGoenka, S. D., Gorzynski, J. E., Shafin, K., Fisk, D. G., Pesout, T., Jensen, T. D., Monlong, J., Chang, P.-C., Baid, G., Bernstein, J. A., Christle, J. W., Dalton, K. P., Garalde, D. R., Grove, M. E., Guillory, J., Kolesnikov, A., Nattestad, M., Ruzhnikov, M. R. Z., Samadi, M., Sethia, A., Spiteri, E., Wright, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B. & Ashley, E. A.
Nat. Biotechnol. (2022).
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Publications
Ultrarapid Nanopore Genome Sequencing in a Critical Care SettingGorzynski, J. E., Goenka, S. D., Shafin, K., Jensen, T. D., Fisk, D. G., Grove, M. E., Spiteri, E., Pesout, T., Monlong, J., Baid, G., Bernstein, J. A., Ceresnak, S., Chang, P.-C., Christle, J. W., Chubb, H., Dalton, K. P., Dunn, K., Garalde, D. R., Guillory, J., Knowles, J. W., Kolesnikov, A., Ma, M., Moscarello, T., Nattestad, M., Perez, M., Ruzhnikov, M. R. Z., Samadi, M., Setia, A., Wright, C., Wusthoff, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B. & Ashley, E. A.
N. Engl. J. Med. (2022).
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Publications
Ultra-Rapid Nanopore Whole Genome Genetic Diagnosis of Dilated Cardiomyopathy in an Adolescent With Cardiogenic ShockGorzynski, J. E., Goenka, S. D., Shafin, K., Jensen, T. D., Fisk, D. G., Grove, M. E., Spiteri, E., Pesout, T., Monlong, J., Bernstein, J. A., Ceresnak, S., Chang, P.-C., Christle, J. W., Chubb, H., Dunn, K., Garalde, D. R., Guillory, J., Ruzhnikov, M. R. Z., Wright, C., Wusthoff, C. J., Xiong, K., Hollander, S. A., Berry, G. J., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B. & Ashley, E. A.
Circ Genom Precis Med 15, e003591 (2022).
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Publications
Pangenomics enables genotyping of known structural variants in 5202 diverse genomesSirén, J., Monlong, J., Chang, X., Novak, A. M., Eizenga, J. M., Markello, C., Sibbesen, J. A., Hickey, G., Chang, P.-C., Carroll, A., Gupta, N., Gabriel, S., Blackwell, T. W., Ratan, A., Taylor, K. D., Rich, S. S., Rotter, J. I., Haussler, D., Garrison, E. & Paten, B.
Science 374 (2021).
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Publications
DeepNull models non-linear covariate effects to improve phenotypic prediction and association powerMcCaw, Z. R., Colthurst, T., Yun, T., Furlotte, N. A., Carroll, A., Alipanahi, B., McLean, C. Y. & Hormozdiari, F.
Nat. Commun. 13, 241 (2022).
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Publications
A population-specific reference panel for improved genotype imputation in African AmericansO’Connell, J., Yun, T., Moreno, M., Li, H., Litterman, N., Kolesnikov, A., Noblin, E., Chang, P.-C.,Shastri, A., Dorfman, E. H., Shringarpure, S., Auton, A., Carroll, A. & McLean, C. Y.
Communications Biology 4, 1–9 (2021).
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Publications
Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-readsShafin, K., Pesout, T., Chang, P.-C., Nattestad, M., Kolesnikov, A., Goel, S., Baid, G., Kolmogorov, M., Eizenga, J. M., Miga, K. H., Carnevali, P., Jain, M., Carroll, A. & Paten, B.
Nat. Methods 18, 1322–1332 (2021). [readcube]
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Publications
DeepConsensus: Gap-Aware Sequence Transformers for Sequence CorrectionBaid, G., Cook, D. E., Shafin, K., Yun, T., Llinares-López, F., Berthet, Q., Wenger, A. M., Rowell, W. J., Nattestad, M., Yang, H., Kolesnikov, A., Töpfer, A., Ammar, W., Vert, J.-P., Vaswani, A., McLean, C. Y., Chang, P.-C. & Carroll, A.
bioRxiv 2021.08.31.458403 (2021).
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Publications
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphologyAlipanahi, B., Hormozdiari, F., Behsaz, B., Cosentino, J., McCaw, Z. R., Schorsch, E., Sculley, D., Dorfman, E. H., Foster, P. J., Peng, L. H., Phene, S., Hammel, N., Carroll, A., Khawaja, A. P. & McLean, C. Y.
Am. J. Hum. Genet. (2021).
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Publications
Accurate, scalable cohort variant calls using DeepVariant and GLnexusYun, T., Li, H., Chang, P-C., Lin, M., Carroll, A., & McLean, C. Y.
Bioinformatics 36, 5582-5589 (2021).
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Publications
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic RegressionYadlowsky, S., Yun, T., McLean, C. & D’Amour, A.
arXiv [stat.ML] (2021).
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Publications
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphologyAlipanahi, B., Hormozdiari, F., Behsaz, B., Cosentino, J., McCaw, Z. R., Schorsch, E., Sculley, D., Dorfman, E. H., Phene, S., Hammel, N., Carroll, A., Khawaja, A. P. & McLean, C. Y.
arXiv [q-bio.GN] (2020).
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Publications
GenomeWarp: an alignment-based variant coordinate transformationMcLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.
Bioinformatics 35, 4389–4391 (2019).
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Publications
Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genomeWenger, A. M., Peluso, P., Rowell, W. J., Chang, P.-C., Hall, R. J., Concepcion, G. T., Ebler, J., Fungtammasan, A., Kolesnikov, A., Olson, N. D., Töpfer, A., Alonge, M., Mahmoud, M., Qian, Y., Chin, C.-S., Phillippy, A. M., Schatz, M. C., Myers, G., DePristo, M. A., Ruan, J., Marschall, T., Sedlazeck, F. J., Zook, J. M., Li, H., Koren, S., Carroll, A., Rank, D. R. & Hunkapiller, M. W.
Nat. Biotechnol. 37, 1155–1162 (2019). [readcube]
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Publications
A universal SNP and small-indel variant caller using deep neural networksPoplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S. S., Dorfman, L., McLean, C. Y. & DePristo, M. A.
Nat. Biotechnol. 36, 983–987 (2018). [readcube]
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Publications
Deep learning of genomic variation and regulatory network dataTelenti, A., Lippert, C., Chang, P.-C. & DePristo, M.
Hum. Mol. Genet. 27, R63–R71 (2018).
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Publications
Sequential regulatory activity prediction across chromosomes with convolutional neural networksKelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y. & Snoek, J.
Genome Res. 28, 739–750 (2018).
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Blog Posts
Expanding research on digital wellbeingby Nicholas Allen
Google Keyword Blog | 23-May-2022
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Advancing health research with Google Health Studiesby Jon Morgan & Paul Eastham
Google Keyword Blog | 9-Dec-2020
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What does electrodermal sensing reveal? Insights from the Pixel Watch & Fitbit Sense 2by Daniel McDuff & Seamus Thomson
Google Research Blog | 24-Oct-2024
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Predicting fetal well-being from cardiotocography signals using AIby Mercy Asiedu & Nichole Young-Lin
Google Research Blog | 27-Sep-2024
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How we built and tested body temperature on Pixel 8 Proby Molly McHugh-Johnson
Google Keyword Blog | 25-Jan-2024
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New Pixel features for a minty fresh start to the yearby Stephanie Scott
Google Keyword Blog | 25-Jan-2024
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Audioplethysmography for cardiac monitoring with hearable devicesby Xiaoran "Van" Fan & Trausti Thormundsson
Google Research Blog | 27-Oct-2023
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The Check Up: our latest health AI developmentsby Greg Corrado
Google Research Blog | 24-Mar-2022
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Enhanced Sleep Sensing in Nest Hubby Michael Dixon & Reena Singhal Lee
Google Research Blog | 9-Nov-2021
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Accelerating Eye Movement Research for Wellness and Accessibilityby Nachiappan Valliappan, & Kai Kohlhoff
Google Research Blog | 10-May-2021
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Need a better night’s sleep? Meet the new Nest Hubby Ashton Udall
Google Keyword Blog | 16-Mar-2021
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Contactless Sleep Sensing in Nest Hubby Michael Dixon & Reena Singhal Lee
Google Research Blog | 16-Mar-2021
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Take a pulse on health and wellness with your phoneby Shwetak Patel
Google Keyword Blog | 4-Feb-2021
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Publications
Smartphone-based gaze estimation for in-home autism researchKim, N. Y., He, J., Wu, Q., Dai, N., Kohlhoff, K., Turner, J., Paul, L. K., Kennedy, D. P., Adolphs, R. & Navalpakkam, V.
Autism Res. 17, 1140–1148 (2024).
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Publications
Soli-enabled noncontact heart rate detection for sleep and meditation trackingXu, L., Lien, J., Li, H., Gillian, N., Nongpiur, R., Li, J., Zhang, Q., Cui, J., Jorgensen, D., Bernstein, A., Bedal, L., Hayashi, E., Yamanaka, J., Lee, A., Wang, J., Shin, D., Poupyrev, I., Thormundsson, T., Pathak, A. & Patel, S.
Sci. Rep. 13, 18008 (2023).
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Publications
Audioplethysmography for Cardiac Monitoring in HearablesFan, X., Pearl, D., Howard, R., Shangguan, L. & Thormundsson, T. APG.
Proceedings of the 29th Annual International Conference on Mobile Computing and Networking 1–15. Association for Computing Machinery (2023).
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Publications
SimPer: Simple Self-Supervised Learning of Periodic TargetsYang, Y., Liu, X., Wu, J., Borac, S., Katabi, D., Poh, M.-Z. & McDuff, D.
arXiv [cs.LG] (ICLR 2023).
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Publications
Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithmsBae, S., Borac, S., Emre, Y., Wang, J., Wu, J., Kashyap, M., Kang, S.-H., Chen, L., Moran, M., Cannon, J., Teasley, E. S., Chai, A., Liu, Y., Wadhwa, N., Krainin, M., Rubinstein, M., Maciel, A., McConnell, M. V., Patel, S., Corrado, G. S., Taylor, J. A., Zhan, J. & Po, M. J.
bioRxiv (2021).
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Publications
LuckyChirp: Opportunistic Respiration Sensing Using Cascaded Sonar on Commodity Devices.Xue, Q. S., Shin, D., Pathak, A., Garrison, J., Hsu, J., Malhotra, M. & Patel, S.
2022 IEEE International Conference on Pervasive Computing and Communications (PerCom) 164–171 (IEEE, 2022).
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Publications
Sleep-wake Detection With a Contactless, Bedside Radar Sleep Sensing SystemDixon, M., Schneider, L. D., Yu, J., Hsu, J., Pathak, A., Shin, D., Lee, R. S., Malhotra, M., Mixter, K., McConnell, M. V., Taylor, J. A., Patel, S. N.,
Google Whitepaper (2021).
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Publications
Digital biomarker of mental fatigueTseng, V. W.-S., Valliappan, N., Ramachandran, V., Choudhury, T. & Navalpakkam, V.
NPJ Digit. Med. 4, 47 (2021).
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Publications
Accelerating eye movement research via accurate and affordable smartphone eye tracking.Valliappan, N., Dai, N., Steinberg, E., He, J., Rogers, K., Ramachandran, V., Xu, P., Shojaeizadeh, M., Guo, L., Kohlhoff, K. & Navalpakkam, V.
Nat. Commun. 11, 4553 (2020).
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Blog Posts [more at Med-PaLM site]
Exploring how AI tools can help increase high-quality health contentby Garth Graham & Viknesh Sounderajah
YouTube Official Blog | 23-Oct-2024
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How gen AI can help doctors and nurses ease their administrative workloadsby Aashima Gupta
Google Keyword Blog | 17-Oct-2024
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Advancing personal health and wellness insights with AIby Shwetak Patel & Shravya Shetty
Google Research Blog | 11-Jun-2024
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Blog Posts
Google Research at Google I/O 2024by Yossi Matias & James Manyika
Google Research Blog | 24-May-2024
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Blog Posts
Advancing medical AI with Med-Geminiby Greg Corrado & Joëlle Barral
Google Keyword Blog | 15-May-2024
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Blog Posts [more at Med-PaLM site]
Our progress on generative AI in healthby Yossi Matias
Google Keyword Blog | 19-Mar-2024
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Blog Posts
3 ways we are building equity into our health workby Dr. Ivor Horn
Google Keyword Blog | 19-Mar-2024
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AMIE: A research AI system for diagnostic medical reasoning and conversationsby Alan Karthikesalingam & Vivek Natarajan
Google Research Blog | 12-Jan-2024
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Blog Posts
3 predictions for AI in healthcare in 2024by Aashima Gupta
Google Keyword Blog | 9-Jan-2024
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Blog Posts
MedLM: generative AI fine-tuned for the healthcare industryby Yossi Matias & Aashima Gupta
Google Cloud Blog | 13-Dec-2023
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Blog Posts [more at Med-PaLM site]
HLTH 2023: Bringing AI to health responsiblyby Michael Howell
Google Keyword Blog | 9-Oct-2023
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Blog Posts
How AI can improve health for everyone, everywhereby Karen DeSalvo
Google Keyword Blog | 3-Oct-2023
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Blog Posts
How 3 healthcare organizations are using generative AIby Aashima Gupta & Greg Corrado
Google Cloud Blog | 29-Aug-2023
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Blog Posts
Multimodal medical AIby Greg Corrado and Yossi Matias
Google Research Blog | 3-Aug-2023
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Blog Posts
Google Research at I/O 2023by James Manyika & Jeff Dean
Google Keyword Blog | 25-May-2023
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Blog Posts
A responsible path to generative AI in healthcareby Aashima Gupta & Amy Waldron
Google Cloud Blog | 13-April-2023
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Blog Posts
Our latest health AI research updatesby Greg Corrado & Yossi Matias
Google Keyword Blog | 14-Mar-2023
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Blog Posts
Google Research, 2022 & beyond: Healthby Greg Corrado & Yossi Matias
Google Research Blog | 23-Feb-2023
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Publications
Scaling wearable foundation modelsNarayanswamy, G., Liu, X., Ayush, K., Yang, Y., Xu, X., Liao, S., Garrison, J., Tailor, S., Sunshine, J., Liu, Y., Althoff, T., Narayanan, S., Kohli, P., Zhan, J., Malhotra, M., Patel, S., Abdel-Ghaffar, S. & McDuff, D.
arXiv [cs.LG] (2024).
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Publications
Towards Democratization of Subspeciality Medical ExpertiseO'Sullivan, J.W., Palepu, A., Saab, K., Weng, W.H., Cheng, Y., Chu, E., Desai, Y., Elezaby, A., Kim, D.S., Lan, R. and Tang, W., 2024.
arXiv preprint arXiv:2410.03741 (2024).
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Publications
A toolbox for surfacing health equity harms and biases in large language modelsPfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., McCoy, L. G., Celi, L. A., Liu, Y., Schaekermann, M., Walton, A., Parrish, A., Nagpal, C., Singh, P., Dewitt, A., Mansfield, P., Prakash, S., Heller, K., Karthikesalingam, A., Semturs, C., Barral, J., Corrado, G., Matias, Y., Smith-Loud, J., Horn, I. & Singhal, K.
Nat Med (2024).
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Publications
PathAlign: A vision-language model for whole slide images in histopathologyAhmed, F., Sellergren, A., Yang, L., Xu, S., Babenko, B., Ward, A., Olson, N., Mohtashamian, A., Matias, Y., Corrado, G. S., Duong, Q., Webster, D. R., Shetty, S., Golden, D., Liu, Y., Steiner, D. F. & Wulczyn, E.
arXiv [cs.CV] (2024).
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Publications
Towards a Personal Health Large Language ModelCosentino, J., Belyaeva, A., Liu, X., Furlotte, N. A., Yang, Z., Lee, C., Schenck, E., Patel, Y., Cui, J., Schneider, L. D., Bryant, R., Gomes, R. G., Jiang, A., Lee, R., Liu, Y., Perez, J., Rogers, J. K., Speed, C., Tailor, S., Walker, M., Yu, J., Althoff, T., Heneghan, C., Hernandez, J., Malhotra, M., Stern, L., Matias, Y., Corrado, G. S., Patel, S., Shetty, S., Zhan, J., Prabhakara, S., McDuff, D. & McLean, C. Y.
arXiv [cs.AI] (2024).
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Publications
Transforming Wearable Data into Health Insights using Large Language Model AgentsMerrill, M. A., Paruchuri, A., Rezaei, N., Kovacs, G., Perez, J., Liu, Y., Schenck, E., Hammerquist, N., Sunshine, J., Tailor, S., Ayush, K., Su, H.-W., He, Q., McLean, C. Y., Malhotra, M., Patel, S., Zhan, J., Althoff, T., McDuff, D. & Liu, X.
arXiv [cs.AI] (2024).
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Publications
Tx-LLM: A Large Language Model for TherapeuticsChaves, J. M. Z., Wang, E., Tu, T., Vaishnav, E. D., Lee, B., Sara Mahdavi, S., Semturs, C., Fleet, D., Natarajan, V. & Azizi, S.
arXiv [cs.CL] (2024).
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Publications
Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM.Li, B., Wang, A., Strachan, P., Séguin, J. A., Lachgar, S., Schroeder, K. C., Fleck, M. S., Wong, R., Karthikesalingam, A., Natarajan, V., Matias, Y., Corrado, G. S., Webster, D., Liu, Y., Hammel, N., Sayres, R., Semturs, C. & Schaekermann, M.
in Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems 1–10 (Association for Computing Machinery, 2024).
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Publications
Advancing Multimodal Medical Capabilities of GeminiYang, L., Xu, S., Sellergren, A., Kohlberger, T., Zhou, Y., Ktena, I., Kiraly, A., Ahmed, F., Hormozdiari, F., Jaroensri, T., Wang, E., Wulczyn, E., Jamil, F., Guidroz, T., Lau, C., Qiao, S., Liu, Y., Goel, A., Park, K., Agharwal, A., George, N., Wang, Y., Tanno, R., Barrett, D. G. T., Weng, W.-H., Sara Mahdavi, S., Saab, K., Tu, T., Kalidindi, S. R., Etemadi, M., Cuadros, J., Sorensen, G., Matias, Y., Chou, K., Corrado, G., Barral, J., Shetty, S., Fleet, D., Ali Eslami, S. M., Tse, D., Prabhakara, S., McLean, C., Steiner, D., Pilgrim, R., Kelly, C., Azizi, S. & Golden, D.
arXiv [cs.CV] (2024).
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Publications
Capabilities of Gemini Models in MedicineSaab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., Chaves, J. Z., Hu, S.-Y., Schaekermann, M., Kamath, A., Cheng, Y., Barrett, D. G. T., Cheung, C., Mustafa, B., Palepu, A., McDuff, D., Hou, L., Golany, T., Liu, L., Alayrac, J.-B., Houlsby, N., Tomasev, N., Freyberg, J., Lau, C., Kemp, J., Lai, J., Azizi, S., Kanada, K., Man, S., Kulkarni, K., Sun, R., Shakeri, S., He, L., Caine, B., Webson, A., Latysheva, N., Johnson, M., Mansfield, P., Lu, J., Rivlin, E., Anderson, J., Green, B., Wong, R., Krause, J., Shlens, J., Dominowska, E., Ali Eslami, S. M., Cui, C., Vinyals, O., Kavukcuoglu, K., Manyika, J., Dean, J., Hassabis, D., Matias, Y., Webster, D., Barral, J., Corrado, G., Semturs, C., Sara Mahdavi, S., Gottweis, J., Karthikesalingam, A. & Natarajan, V.
arXiv [cs.AI] (2024).
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Publications
Towards Generalist Biomedical AITu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., Mustafa, B., Chowdhery, A., Liu, Y., Kornblith, S., Fleet, D., Mansfield, P., Prakash, S., Wong, R., Virmani, S., Semturs, C., Sara Mahdavi, S., Green, B., Dominowska, E., Aguera y Arcas, B., Barral, J., Webster, D., Corrado, G. S., Matias, Y., Singhal, K., Florence, P., Karthikesalingam, A. & Natarajan, V.
NEJM AI (2024).
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Publications
Towards Conversational Diagnostic AITu, T., Palepu, A., Schaekermann, M., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Tomasev, N., Azizi, S., Singhal, K., Cheng, Y., Hou, L., Webson, A., Kulkarni, K., Sara Mahdavi, S., Semturs, C., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Karthikesalingam, A. & Natarajan, V.
arXiv [cs.AI] (2024).
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Publications
LLMs Accelerate Annotation for Medical Information Extraction.Goel, A., Gueta, A., Gilon, O., Liu, C., Erell, S., Nguyen, L. H., Hao, X., Jaber, B., Reddy, S., Kartha, R., Steiner, J., Laish, I. & Feder, A.
arXiv [cs.CL] (2023).
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Publications
Towards Accurate Differential Diagnosis with Large Language Models.McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., Hou, L., Cheng, Y., Liu, Y., Sara Mahdavi, S., Prakash, S., Pathak, A., Semturs, C., Patel, S., Webster, D. R., Dominowska, E., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Sunshine, J., Karthikesalingam, A. & Natarajan, V.
arXiv [cs.CY] (2023).
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Publications
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generationTanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Singhal, K., Azizi, S., Tu, T., Schaekermann, M., May, R., Lee, R., Man, S., Ahmed, Z., Mahdavi, S., Belgrave, D., Natarajan, V., Shetty, S., Kohli, P., Huang, P.-S., Karthikesalingam, A. & Ktena, I.
arXiv [eess.IV] (2023).
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Publications
The Capability of Large Language Models to Measure Psychiatric FunctioningGalatzer-Levy, I. R., McDuff, D., Natarajan, V., Karthikesalingam, A. & Malgaroli, M.
arXiv [cs.CL] (2023).
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Publications
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encodersXu, S., Yang, L., Kelly, C., Sieniek, M., Kohlberger, T., Ma, M., Weng, W.-H., Kiraly, A., Kazemzadeh, S., Melamed, Z., Park, J., Strachan, P., Liu, Y., Lau, C., Singh, P., Chen, C., Etemadi, M., Kalidindi, S. R., Matias, Y., Chou, K., Corrado, G. S., Shetty, S., Tse, D., Prabhakara, S., Golden, D., Pilgrim, R., Eswaran, K. & Sellergren, A.
arXiv [cs.CV] (2023).
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Publications
Multimodal LLMs for health grounded in individual-specific dataBelyaeva, A., Cosentino, J., Hormozdiari, F., Eswaran, K., Shetty, S., Corrado, G., Carroll, A., McLean, C. Y. & Furlotte, N. A.
arXiv [q-bio.QM] (2023).
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Publications
Large Language Models are Few-Shot Health LearnersLiu, X., McDuff, D., Kovacs, G., Galatzer-Levy, I., Sunshine, J., Zhan, J., Poh, M.-Z., Liao, S., Di Achille, P. & Patel, S.
arXiv [cs.CL] (2023).
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Publications
Towards Expert-Level Medical Question Answering with Large Language ModelsSinghal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., Schaekermann, M., Wang, A., Amin, M., Lachgar, S., Mansfield, P., Prakash, S., Green, B., Dominowska, E., Aguera y Arcas, B., Tomasev, N., Liu, Y., Wong, R., Semturs, C., Sara Mahdavi, S., Barral, J., Webster, D., Corrado, G. S., Matias, Y., Azizi, S., Karthikesalingam, A. & Natarajan, V.
arXiv [cs.CL] (2023).
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Publications
Large Language Models Encode Clinical KnowledgeSinghal, K., Azizi, S., Tu, T., Sara Mahdavi, S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Scharli, N., Chowdhery, A., Mansfield, P., Aguera y Arcas, B., Webster, D., Corrado, G. S., Matias, Y., Chou, K., Gottweis, J., Tomasev, N., Liu, Y., Rajkomar, A., Barral, J., Semturs, C., Karthikesalingam, A. & Natarajan, V.
Nature (2023).
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Blog Posts
Advancing personal health and wellness insights with AIby Shwetak Patel & Shravya Shetty
Google Research Blog | 11-Jun-2024
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Blog Posts
This AI model is helping researchers detect disease based on coughsby Shravya Shetty
Google Research Blog | 19-Aug-2024
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Blog Posts
Joint Speech Recognition and Speaker Diarization via Sequence Transductionby Laurent El Shafey and Izhak Shafran
Google Research Blog | 16-Aug-2019
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Blog Posts
How AI can improve products for people with impaired speechby Julie Cattiau
Google Keyword Blog | 7-May-2019
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Blog Posts
Understanding Medical Conversationsby Katherine Chou & Chung-Cheng Chiu
Google Research Blog | 21-Nov-2017
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Publications
HeAR -- Health Acoustic RepresentationsBaur, S., Nabulsi, Z., Weng, W.-H., Garrison, J., Blankemeier, L., Fishman, S., Chen, C., Kakarmath, S., Maimbolwa, M., Sanjase, N., Shuma, B., Matias, Y., Corrado, G. S., Patel, S., Shetty, S., Prabhakara, S., Muyoyeta, M. & Ardila, D.
arXiv [cs.LG] (2024).
-
Publications
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic SignalsBlankemeier, L., Baur, S., Weng, W.-H., Garrison, J., Matias, Y., Prabhakara, S., Ardila, D. & Nabulsi, Z.
arXiv [cs.LG] (2023).
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Publications
Medical Scribe: Corpus Development and Model Performance AnalysesShafran, I., Du, N., Tran, L., Perry, A., Keyes, L., Knichel, M., Domin, A., Huang, L., Chen, Y., Li, G., Wang, M., El Shafey, L., Soltau, H. & Paul, J. S.
Proceedings of the Language Resources and Evaluation Conference. arXiv [cs.CL] (2020).
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Publications
Extracting Symptoms and their Status from Clinical ConversationsDu, N., Chen, K., Kannan, A., Tran, L., Chen, Y. & Shafran, I.
Proceedings of the Annual Meeting of the Association of Computational Linguistics. arXiv [cs.LG] (2019).
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Publications
Automatically Charting Symptoms From Patient-Physician Conversations Using Machine LearningRajkomar, A., Kannan, A., Chen, K., Vardoulakis, L., Chou, K., Cui, C., & Dean, J.
JAMA Intern. Med. 179, 836–838 (2019).
-
Publications
Joint Speech Recognition and Speaker Diarization via Sequence TransductionEl Shafey, L., Soltau, H. & Shafran, I.
Proceedings of Interspeech. arXiv [cs.CL] (2019).
-
Publications
Learning to Infer Entities, Properties and their Relations from Clinical ConversationsDu, N., Wang, M., Tran, L., Li, G. & Shafran, I.
Proc. Empirical Methods in Natural Language Processing. arXiv [cs.CL] (2019).
-
Publications
Speech recognition for medical conversationsChiu, C.-C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., Kannan, A., Nguyen, P., Sak, H., Sankar, A., Tansuwan, J., Wan, N., Wu, Y., & Zhang X.
arXiv [cs.CL] (2017).
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Blog Posts
Deciphering clinical abbreviations with privacy protecting MLby Alvin Rajkoma and Eric Loreaux
Google Research Blog | 24-Jan-2023
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Blog Posts
EHR-Safe: Generating High-Fidelity and Privacy-Preserving Synthetic Electronic Health Recordsby Jinsung Yoon and Sercan O. Arik
Google Research Blog | 21-Dec-2022
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Blog Posts
Multi-task Prediction of Organ Dysfunction in ICUsby Subhrajit Roy & Diana Mincu
Google Research Blog | 22-Jul-2021
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Blog Posts
A Step Towards Protecting Patients from Medication Errorsby Kathryn Rough & Alvin Rajkomar
Google Research Blog | 2-Apr-2020
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Blog Posts
Expanding the Application of Deep Learning to Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
Google Research Blog | 22-Jan-2019
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Blog Posts
Scaling Streams with Googleby Demis Hassabis & Mustafa Suleyman & Dominic King
DeepMind Blog | 13-Nov-2018
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Blog Posts
Deep Learning for Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
Google Research Blog | 8-May-2018
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Blog Posts
Making Healthcare Data Work Better with Machine Learningby Patrik Sundberg & Eyal Oren
Google Research Blog | 2-Mar-2018
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Publications
User-centred design for machine learning in health care: a case study from care managementSeneviratne, M. G., Li, R. C., Schreier, M., Lopez-Martinez, D., Patel, B. S., Yakubovich, A., Kemp, J. B., Loreaux, E., Gamble, P., El-Khoury, K., Vardoulakis, L., Wong, D., Desai, J., Chen, J. H., Morse, K. E., Downing, N. L., Finger, L. T., Chen, M.-J. & Shah, N.
BMJ Health Care Inform 29, (2022).
-
Publications
Boosting the interpretability of clinical risk scores with intervention predictionsLoreaux, E., Yu, K., Kemp, J., Seneviratne, M., Chen, C., Roy, S., Protsyuk, I., Harris, N., D’Amour, A., Yadlowsky, S. & Chen, M.-J.
arXiv [cs.LG] (2022).
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Publications
Deciphering clinical abbreviations with a privacy protecting machine learning systemRajkomar, A., Loreaux, E., Liu, Y., Kemp, J., Li, B., Chen, M.-J., Zhang, Y., Mohiuddin, A. & Gottweis, J.
Nat. Commun. 13, 7456 (2022).
-
Publications
Structured understanding of assessment and plans in clinical documentationStupp, D., Barequet, R., Lee, I.-C., Oren, E., Feder, A., Benjamini, A., Hassidim, A., Matias, Y., Ofek, E. & Rajkomar, A.
bioRxiv (2022).
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Publications
Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routingRoy, S., Mincu, D., Loreaux, E., Mottram, A., Protsyuk, I., Harris, N., Xue, Y., Schrouff, J., Montgomery, H., Connell, A., Tomasev, N., Karthikesalingam, A. & Seneviratne, M.
J. Am. Med. Inform. Assoc. (2021).
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Publications
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health recordsTomašev, N., Harris, N., Baur, S., Mottram, A., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Magliulo, V., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Osborne, T. F., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Seneviratne, M. G., Ledsam, J. R. & Mohamed, S.
Nat. Protoc. 1–23 (2021). [readcube]
-
Publications
Learning to Select Best Forecast Tasks for Clinical Outcome PredictionXue Y, Du N, Mottram A, Seneviratne A, Dai AM.
NeurIPS (2020).
-
Publications
Deep State-Space Generative Model For Correlated Time-to-Event PredictionsXue Y, Zhou D, Du N, Dai A, Xu Z, Zhang K, Cui C.
KDD (2020).
-
Publications
Graph convolutional transformer: Learning the graphical structure of electronic health recordsChoi E, Xu Z, Li Y, Dusenberry MW, Flores G, Xue Y, Dai AM.
AAAI (2020).
-
Publications
Analyzing the role of model uncertainty for electronic health recordsDusenberry MW, Tran D, Choi E, Kemp J, Nixon J, Jerfel G, Heller K, & Dai AM.
ACM CHIL (2020).
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Publications
Explaining an increase in predicted risk for clinical alertsHardt M, Rajkomar A, Flores G, Dai A, Howell M, Corrado G, Cui C, & Hardt M.
ACM CHIL (2020).
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Publications
Predicting inpatient medication orders from electronic health record dataRough, K., Dai, A. M., Zhang, K., Xue, Y., Vardoulakis, L. M., Cui, C., Butte, A. J., Howell, M. D. & Rajkomar, A.
Clin. Pharmacol. Ther. 108, 145–154 (2020).
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Publications
A clinically applicable approach to continuous prediction of future acute kidney injuryTomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R. & Mohamed, S.
Nature 572, 116–119 (2019). [readcube]
-
Publications
Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissionsConnell, A., Montgomery, H., Martin, P., Nightingale, C., Sadeghi-Alavijeh, O., King, D., Karthikesalingam, A., Hughes, C., Back, T., Ayoub, K., Suleyman, M., Jones, G., Cross, J., Stanley, S., Emerson, M., Merrick, C., Rees, G., Laing, C. & Raine, R.
npj Digit Med 2, 67 (2019).
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Publications
Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care CostsConnell A., Raine R., Martin P., Barbosa E.C., Morris S., Nightingale C., Sadeghi-Alavijeh O., King D., Karthikesalingam A., Hughes C., Back T., Ayoub K., Suleyman M., Jones G., Cross J., Stanley S., Emerson M., Merrick C., Rees G., Montgomery H., & Laing C.
J Med Internet Res 21(7):e13147 (2019).
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Publications
Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care ProfessionalsConnell A, Black G, Montgomery H, Martin P, Nightingale C, King D, Karthikesalingam A, Hughes C, Back T, Ayoub K, Suleyman M, Jones G, Cross J, Stanley S, Emerson M, Merrick C, Rees G, Laing C, & Raine R.
J Med Internet Res 21(7):e13143 (2019).
-
Publications
Improved Patient Classification with Language Model Pretraining Over Clinical NotesKemp J, Rajkomar A, & Dai AM.
arXiv [cs.LG] (2019).
-
Publications
Federated and Differentially Private Learning for Electronic Health RecordsPfohl SR, Dai AM, & Heller K.
arXiv [cs.LG] (2019).
-
Publications
Deep Physiological State Space Model for Clinical ForecastingXue Y, Zhou D, Du N, Dai AM, Xu Z, Zhang K,& Cui C.
arXiv [cs.LG] (2019).
-
Publications
Modelling EHR timeseries by restricting feature interactionZhang K, Xue Y, Flores G, Rajkomar A, Cui C, & Dai AM.
arXiv [cs.LG] (2019).
-
Publications
Scalable and accurate deep learning with electronic health recordsRajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J.
npj Digital Med 1, 18 (2018).
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Blog Posts
A step towards making heart health screening accessible for billions with PPG signalsby Mayank Daswani & Sujay Kakarmath
Google Research Blog | 25-Jul-2024
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Blog Posts
Using generative AI to investigate medical imagery models and datasetsby Oran Lang & Heather Cole-Lewis
Google Research Blog | 5-Jun-2024
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Blog Posts
Developing an aging clock using deep learning on retinal imagesby Sara Ahadi & Andrew Carroll
Google Research Blog | 11-Apr-2023
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Blog Posts
Detecting novel systemic biomarkers in external eye photoby Boris Babenko & Akib Uddin
Google Research Blog | 24-Mar-2023
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
Google Research Blog | 24-Mar-2022
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Blog Posts
Detecting Signs of Disease from External Images of the Eyeby Boris Babenko & Naama Hammel
Google Research Blog | 24-Mar-2022
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Blog Posts
How AI could predict sight-threatening eye conditionsby Terry Spitz & Jim Winkens
Google Keyword Blog | 18-May-2020
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Blog Posts
Using AI to predict retinal disease progressionby Jason Yim, Reena Chopra, Jeffrey De Fauw & Joseph Ledsam
DeepMind Blog | 18-May-2020
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Blog Posts
Detecting hidden signs of anemia from the eyeby Akinori Mitani
Google Keyword Blog | 28-Jan-2020
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Blog Posts
Assessing Cardiovascular Risk Factors with Computer Visionby Lily Peng
Google Research Blog | 2-Feb-2018
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Publications
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep LearningWeng, W.-H., Baur, S., Daswani, M., Chen, C., Harrell, L., Kakarmath, S., Jabara, M., Behsaz, B., McLean, C. Y., Matias, Y., Corrado, G. S., Shetty, S., Prabhakara, S., Liu, Y., Danaei, G. & Ardila, D.
PLOS Global Public Health. 4(6): e0003204 (2024).
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Publications
Using generative AI to investigate medical imagery models and datasetsLang, O., Yaya-Stupp, D., Traynis, I., Cole-Lewis, H., Bennett, C. R., Lyles, C. R., Lau, C., Irani, M., Semturs, C., Webster, D. R., Corrado, G. S., Hassidim, A., Matias, Y., Liu, Y., Hammel, N. & Babenko, B.
EBioMedicine 102, 105075 (2024).
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Publications
Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clockAhadi, S., Wilson, K. A., Jr, Babenko, B., McLean, C. Y., Bryant, D., Pritchard, O., Kumar, A., Carrera, E. M., Lamy, R., Stewart, J. M., Varadarajan, A., Berndl, M., Kapahi, P. & Bashir, A.
Elife 12, (2023).
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Publications
A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.Babenko, B., Traynis, I., Chen, C., Singh, P., Uddin, A., Cuadros, J., Daskivich, L. P., Maa, A. Y., Kim, R., Kang, E. Y.-C., Matias, Y., Corrado, G. S., Peng, L., Webster, D. R., Semturs, C., Krause, J., Varadarajan, A. V., Hammel, N. & Liu, Y.
The Lancet Digital Health (2023).
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Publications
Detection of signs of disease in external photographs of the eyes via deep learningBabenko, B., Mitani, A., Traynis, I., Kitade, N., Singh, P., Maa, A. Y., Cuadros, J., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A., Hammel, N. & Liu, Y.
Nat Biomed Eng (2022).
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Publications
Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation studyLiu, X., Ali, T. K., Singh, P., Shah, A., McKinney, S. M., Ruamviboonsuk, P., Turner, A. W., Keane, P. A., Chotcomwongse, P., Nganthavee, V., Chia, M., Huemer, J., Cuadros, J., Raman, R., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Varadarajan, A. V., Liu, Y., Chopra, R. & Bavishi, P.
Ophthalmol Retina (2022).
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Publications
Retinal fundus photographs capture hemoglobin loss after blood donationMitani, A., Traynis, I., Singh, P., Corrado, G. S., Webster, D. R., Peng, L. H., Varadarajan, A. V., Liu, Y. & Hammel, N.
doi:10.1101/2021.12.30.21268488
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Publications
Predicting the risk of developing diabetic retinopathy using deep learningBora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A. V., Hammel, N., Liu, Y. & Bavishi, P.
The Lancet Digital Health (2020). doi:10.1016/S2589-7500(20)30250-8
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Publications
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learningMoraes, G., Fu, D. J., Wilson, M., Khalid, H., Wagner, S. K., Korot, E., Ferraz, D., Faes, L., Kelly, C. J., Spitz, T., Patel, P. J., Balaskas, K., Keenan, T. D. L., Keane, P. A. & Chopra, R.
Ophthalmology (2020). doi:10.1016/j.ophtha.2020.09.025
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Publications
Scientific Discovery by Generating Counterfactuals Using Image TranslationNarayanaswamy, A., Venugopalan, S., Webster, D. R., Peng, L., Corrado, G. S., Ruamviboonsuk, P., Bavishi, P., Brenner, M., Nelson, P. C. & Varadarajan, A. V.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 273–283 (2020). doi:10.1007/978-3-030-59710-8_27 arXiv
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Publications
Predicting conversion to wet age-related macular degeneration using deep learningYim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., Moraes, G., Meyer, C., Wilson, M., Dixon, J., Hughes, C., Rees, G., Khaw, P. T., Karthikesalingam, A., King, D., Hassabis, D., Suleyman, M., Back, T., Ledsam, J. R., Keane, P. A. & De Fauw, J.
Nat. Med. (2020). [readcube]
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Publications
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learningVaradarajan, A. V., Bavishi, P., Ruamviboonsuk, P., Chotcomwongse, P., Venugopalan, S., Narayanaswamy, A., Cuadros, J., Kanai, K., Bresnick, G., Tadarati, M., Silpa-Archa, S., Limwattanayingyong, J., Nganthavee, V., Ledsam, J. R., Keane, P. A., Corrado, G. S., Peng, L. & Webster, D. R.
Nat. Commun. 11, 130 (2020).
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Publications
Detection of anaemia from retinal fundus images via deep learningMitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y. & Varadarajan, A. V.
Nat Biomed Eng (2019). [readcube]
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Publications
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep LearningBabenko, B., Balasubramanian, S., Blumer, K. E., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N. & Varadarajan, A. V.
arXiv [cs.CV] (2019).
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Publications
Deep Learning for Predicting Refractive Error From Retinal Fundus Imagesaradarajan, A.V., Poplin, R., Blumer, K., Angermueller, C., Lesdam, J., Chopra, R., Keane, P.A., Corrado, G. S., Peng, L., Webster, D. R.
Invest. Ophthalmol. Vis. Sci. 59, 2861–2868 (2018).
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Publications
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningPoplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R.
Nat. Biomed. Eng. 2, 158–164 (2018). [readcube]
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Blog Posts [more at OHS Blog]
5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia & Shravya Shetty
Google Keyword Blog | 31-Oct-2023
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Engaging with the Healthcare Developer Ecosystem in Indiaby Richa Tiwari
Open Health Stack Blog | 10-Oct-2023.
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Empowering Developers to Build Next Generation, Mobile-First Healthcare Solutionsby Richa Tiwari
Open Health Stack Blog | 2-Sep-2023.
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Manage FHIR Data from Android App with Open Health Stack and Google Cloudby Abirami Sukumaran & Omar Ismail
Google Cloud Blog | 17-Aug-2023.
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7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
Google Keyword Blog | 18-Jul-2023
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Our collaboration with WHO to improve public healthby Karen DeSalvo
Google Keyword Blog | 23-May-2023
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New tools to help developers build better health appsby Fred Hersch
Google Keyword Blog | 14-Mar-2023.
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Our FHIR SDK for Android Developersby Katherine Chou & Sudhi Herle
Android Developers Blog | 24-Mar-2022
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Working with the WHO to power digital health appsby Fred Hersch & Jing Tang
Google Keyword Blog | 8-Dec-2021
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Publications
A full-STAC remedy for global digital health transformation: open standards, technologies, architectures and contentMehl, G. L., Seneviratne, M. G., Berg, M. L., Bidani, S., Distler, R. L., Gorgens, M., Kallander, K. E., Labrique, A. B., Landry, M. S., Leitner, C., Lubell-Doughtie, P. B., Marcelo, A. D., Matias, Y., Nelson, J., Nguyen, V., Nsengimana, J. P., Orton, M., Otzoy Garcia, D. R., Oyaole, D. R., Ratanaprayul, N., Roth, S., Schaefer, M. P., Settle, D., Tang, J., Tien-Wahser, B., Wanyee, S. & Hersch, F.
Oxf Open Digit Health, (2023).
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Collaborative Momentum: The 2023 State of the Digital Public Goods Ecosystem ReportDigital Public Goods Alliance - Promoting digital public goods to create a more equitable world (2023). 14-Dec-2023.
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Health-specific embedding tools for dermatology and pathologyby Dave Steiner & Rory Pilgrim
Google Research Blog | 8-Mar-2024
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Accelerate AI development for Digital Pathology using EZ WSI DICOMWeb Python libraryGoogle Open Source Blog | 17-May-2023
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Using AI to Predict the Presence of Cancer Spreadby Justin Krogue, Yun Liu, Po-Hsuan Cameron Chen & Ellery A
Nature Portfolio Health Community Blog | 10-May-2023
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Learning from deep learning: a case study of feature discovery and validation in pathologyby Ellery Wulczyn and Yun Liu
Google Research Blog | 14-Mar-2023
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Pathology digitization and the fight against cancerby Karen DeSalvo
Google Cloud Blog | 12-Dec-2022
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Verily and Lumea Announce Development Partnership to Advance Digital Pathology in Prostate CancerVerily Blog | 16-Mar-2022
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An International Scientific Challenge for the Diagnosis and Gleason Grading of Prostate Cancerby Po-Hsuan Cameron Chen & Maggie Demkin
Google Research Blog | 11-Feb-2022
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The promise of using AI to help prostate cancer careby Po-Hsuan Cameron Chen & Yun Liu
Google Keyword Blog | 23-Sept-2021
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PAIR @ CHI 2021by People + AI Research
People + AI Research Blog | 14-May-2021
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Blog Posts
Learning from deep learning: developing interpretable AI approaches in histopathology to predict patient prognosis and explore novel featuresby Dave Steiner, Yun Liu, Craig Mermel, Kurt Zatloukal, Heimo Muller, Markus Plass
npj Digital Medicine Blog | 19-Apr-2021
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Blog Posts
Defense Innovation Unit Selects Google Cloud to Help U.S. Military Health System with Predictive Cancer DiagnosesGoogle Cloud Blog | 2-Sep-2020
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Using AI to identify the aggressiveness of prostate cancerby Kunal Nagpal & Craig Mermel
Google Keyword Blog | 23-Jul-2020
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Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
Google Research Blog | 19-Feb-2020
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Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathologyby Narayan Hedge & Carrie Cai
Google Research Blog | 19-July-2019
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Improved Grading of Prostate Cancer Using Deep Learningby Martin Stumpe & Craig Mermel
Google Research Blog | 16-Nov-2018
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Applying Deep Learning to Metastatic Breast Cancer Detectionby Martin Stumpe & Craig Mermel
Google Research Blog | 12-Oct-2018
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An Augmented Reality Microscope for Cancer Detectionby Martin Stumpe & Craig Mermel
Google Research Blog | 16-Apr-2018
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Assisting Pathologists in Detecting Cancer with Deep Learningby Martin Stumpe & Lily Peng
Google Research Blog | 3-Mar-2017
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Publications
PathAlign: A vision-language model for whole slide images in histopathologyAhmed, F., Sellergren, A., Yang, L., Xu, S., Babenko, B., Ward, A., Olson, N., Mohtashamian, A., Matias, Y., Corrado, G. S., Duong, Q., Webster, D. R., Shetty, S., Golden, D., Liu, Y., Steiner, D. F. & Wulczyn, E.
arXiv [cs.CV] (2024)
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Publications
Estrogen receptor gene expression prediction from H&E whole slide imagesSrinivas, A. A., Jaroensri, R., Wulczyn, E., Liu, Y., Wren, J. H., Thompson, E. E., Olson, N., Beckers, F., Miao, M., Chen, P.-H. C. & Steiner, D. F.
bioRxiv (2024).
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Publications
An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual HistologyMcNeil, C., Wong, P. F., Sridhar, N., Wang, Y., Santori, C., Wu, C.-H., Homyk, A., Gutierrez, M., Behrooz, A., Tiniakos, D., Burt, A. D., Pai, R. K., Tekiela, K., Cameron Chen, P.-H., Fischer, L., Martins, E. B., Seyedkazemi, S., Freedman, D., Kim, C. C. & Cimermancic, P.
Mod. Pathol. 37, 100377 (2023).
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Publications
Domain-specific optimization and diverse evaluation of self-supervised models for histopathologyLai, J., Ahmed, F., Vijay, S., Jaroensri, T., Loo, J., Vyawahare, S., Agarwal, S., Jamil, F., Matias, Y., Corrado, G. S., Webster, D. R., Krause, J., Liu, Y., Chen, P.-H. C., Wulczyn, E. & Steiner, D. F.
arXiv [eess.IV] (2023).
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Publications
Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learningKrogue, J. D., Azizi, S., Tan, F., Flament-Auvigne, I., Brown, T., Plass, M., Reihs, R., Müller, H., Zatloukal, K., Richeson, P., Corrado, G. S., Peng, L. H., Mermel, C. H., Liu, Y., Chen, P.-H. C., Gombar, S., Montine, T., Shen, J., Steiner, D. F. & Wulczyn, E.
Commun. Med. 3, 59 (2023).
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Publications
Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk StratificationL’Imperio, V., Wulczyn, E., Plass, M., Müller, H., Tamini, N., Gianotti, L., Zucchini, N., Reihs, R., Corrado, G. S., Webster, D. R., Peng, L. H., Chen, P.-H. C., Lavitrano, M., Liu, Y., Steiner, D. F., Zatloukal, K. & Pagni, F.
JAMA Netw Open 6, e2254891 (2023).
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Publications
Deep learning models for histologic grading of breast cancer and association with disease prognosisJaroensri, R., Wulczyn, E., Hegde, N., Brown, T., Flament-Auvigne, I., Tan, F., Cai, Y., Nagpal, K., Rakha, E. A., Dabbs, D. J., Olson, N., Wren, J. H., Thompson, E. E., Seetao, E., Robinson, C., Miao, M., Beckers, F., Corrado, G. S., Peng, L. H., Mermel, C. H., Liu, Y., Steiner, D. F. & Chen, P.-H. C.
npj Breast Cancer 8, 1–12 (2022).
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challengeBulten, W., Kartasalo, K., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D. F., van Boven, H., Vink, R., Hulsbergen-van de Kaa, C., van der Laak, J., Amin, M. B., Evans, A. J., van der Kwast, T., Allan, R., Humphrey, P. A., Grönberg, H., Samaratunga, H., Delahunt, B., Tsuzuki, T., Häkkinen, T., Egevad, L., Demkin, M., Dane, S., Tan, F., Valkonen, M., Corrado, G. S., Peng, L., Mermel, C. H., Ruusuvuori, P., Litjens, G. & Eklund, M.
Nat. Med. 1–10 (2022).
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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology imagesSadhwani, A., Chang, H.-W., Behrooz, A., Brown, T., Auvigne-Flament, I., Patel, H., Findlater, R., Velez, V., Tan, F., Tekiela, K., Wulczyn, E., Yi, E. S., Mermel, C. H., Hanks, D., Chen, P.-H. C., Kulig, K., Batenchuk, C., Steiner, D. F. & Cimermancic, P.
Sci. Rep. 11, 1–11 (2021).
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Determining breast cancer biomarker status and associated morphological features using deep learningGamble, P., Jaroensri, R., Wang, H., Tan, F., Moran, M., Brown, T., Flament-Auvigne, I., Rakha, E. A., Toss, M., Dabbs, D. J., Regitnig, P., Olson, N., Wren, J. H., Robinson, C., Corrado, G. S., Peng, L. H., Liu, Y., Mermel, C. H., Steiner, D. F. & Chen, P.-H. C.
Communications Medicine 1, 1–12 (2021).
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Publications
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason gradingWulczyn, E., Nagpal, K., Symonds, M., Moran, M., Plass, M., Reihs, R., Nader, F., Tan, F., Cai, Y., Brown, T., Flament-Auvigne, I., Amin, M. B., Stumpe, M. C., Müller, H., Regitnig, P., Holzinger, A., Corrado, G. S., Peng, L. H., Chen, P.-H. C., Steiner, D. F., Zatloukal, K., Liu, Y. & Mermel, C. H.
Communications Medicine 1, 1–8 (2021).
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Publications
Onboarding Materials as Boundary Objects for Developing AI AssistantsCai, C.J., Steiner, D., Wilcox, L., Terry, M. and Winter, S.
Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, ACM (2021).
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Publications
Interpretable survival prediction for colorectal cancer using deep learningWulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., Hegde, N., Sadhwani, A., MacDonald, R., Ayalew, B., Corrado, G. S., Peng, L. H., Tse, D., Müller, H., Xu, Z., Liu, Y., Stumpe, M. C., Zatloukal, K. & Mermel, C. H.
npj Digital Medicine 4, 1–13 (2021).
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Publications
Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate BiopsiesSteiner, D. F., Nagpal, K., Sayres, R., Foote, D. J., Wedin, B. D., Pearce, A., Cai, C. J., Winter, S. R., Symonds, M., Yatziv, L., Kapishnikov, A., Brown, T., Flament-Auvigne, I., Tan, F., Stumpe, M. C., Jiang, P.-P., Liu, Y., Chen, P.-H. C., Corrado, G. S., Terry, M. & Mermel, C. H.
JAMA Netw Open 3, e2023267–e2023267 (2020).
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Publications
Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy SpecimensNagpal, K., Foote, D., Tan, F., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N., Olson, N., Smith, J. L., Mohtashamian, A., Peterson, B., Amin, M. B., Evans, A. J., Sweet, J. W., Cheung, C., van der Kwast, T., Sangoi, A. R., Zhou, M., Allan, R., Humphrey, P. A., Hipp, J. D., Gadepalli, K., Corrado, G. S., Peng, L. H., Stumpe, M. C. & Mermel, C. H.
JAMA Oncol (2020).
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Deep learning-based survival prediction for multiple cancer types using histopathology imagesWulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y. & Stumpe, M. C.
PLOS ONE 15, e0233678 (2020).
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Publications
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer DetectionKohlberger, T., Liu, Y., Moran, M., Chen, P.-H. C., Brown, T., Hipp, J. D., Mermel, C. H. & Stumpe, M. C.
J. Pathol. Inform. 10, 39 (2019).
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Publications
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosisChen, P.C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G.S., Hipp, J.D., Mermel, C.H., Stumpe, M. C.
Nat Med 25, 1453–1457 (2019). [readcube]
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Publications
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for PathologistsLiu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L.H., Hipp, J.D., Stumpe, M.C. (2019).
Arch. Pathol. Lab. Med. 143, 859–868 (2019).
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Publications
Similar image search for histopathology: SMILYHegde, N., Hipp, J. D., Liu, Y., Emmert-Buck, M., Reif, E., Smilkov, D., Terry, M., Cai, C. J., Amin, M. B., Mermel, C. H., Nelson, P. Q., Peng, L. H., Corrado, G. S. & Stumpe, M. C.
npj Digit Med 2, 56 (2019).
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Publications
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-MakingCai, C.J., Winter, S., Steiner, D., Wilcox, L. and Terry, M.
Proceedings of the ACM on Human-computer Interaction, 3(CSCW), pp.1-24 (2019)
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Publications
Human-centered tools for coping with imperfect algorithms during medical decision-makingCai, C.J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G.S., Stumpe, M.C. and Terry, M.
In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14) (2019).
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Publications
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancerNagpal, K., Foote, D., Liu, Y., Chen, P.H.C., Wulczyn, E., Tan, F., Olson, N., Smith, J.L., Mohtashamian, A., Wren, J.H., Corrado, G.S., MacDonald, R., Peng, L. H., Amin, M.B., Evans, A.J., Sanjoi, A.R., Mermel, C. H., Hipp, J. D., Stumpe, M. C.
npj Digit. Med. 2, 48 (2019).
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Publications
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast CancerSteiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., Stumpe, M.C.
Am. J. Surg. Pathol. 42, 1636–1646 (2018).
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Publications
Detecting cancer metastases on gigapixel pathology imagesLiu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., Corrado, G.S. and Hipp, J.D., Peng, L., Stumpe, M. C.
arXiv preprint arXiv:1703.02442 (2017).
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Blog Posts
How we’re using AI to make emergency healthcare more accessibleby Shravya Shetty
Google Keyword Blog | 24-Oct-2024
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Blog Posts
How AI is helping advance women’s health around the worldGoogle Keyword Blog | 8-Mar-2024
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Blog Posts
New anonymized smartphone data reveals it often takes more time than expected to access healthcare in the real world: Using aggregated and anonymized data from over 100 countries to quantify inequities in access to healthcareby Kristina Gligoric
Nature Portfolio Health Community Blog | 22-Nov-2023
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Blog Posts
5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia & Shravya Shetty
Google Keyword Blog | 31-Oct-2023
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Blog Posts
How we’re using AI to combat floods, wildfires and extreme heatby Yossi Matias
Google Keyword | 10-Oct-2023
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Blog Posts
How we’re supporting access to emergency maternal care in Nigeriaby Charlotte Stanton
Google Africa Blog | 9-May-2023
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Blog Posts
How we’re helping people and cities adapt to extreme heatby Kate Brandt
Google Keyword Blog | 29-Mar-2023
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Blog Posts
New tools to support vaccine access and distributionby Tomer Shekel
Google Keyword Blog | 9-Jun-2021
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Blog Posts
An update on our efforts to help Americans navigate COVID-19by Ruth Porat
Google Keyword Blog | 27-Oct-2020
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Blog Posts
Making data useful for public healthby Katherine Chou
Google Keyword Blog | 17-Sept-2020
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Blog Posts
Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
Google Keyword Blog | 3-Apr-2020
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Blog Posts
Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
Google Keyword Blog | 3-Apr-2020
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Blog Posts
New Insights into Human Mobility with Privacy Preserving Aggregationby Adam Sadilek & Xerxes Dotiwalla
Google Research Blog | 12-Nov-2019
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Publications
Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location dataYoung, M. T., Vispute, S., Serghiou, S., Kumok, A., Shah, Y., Lane, K. J., Black-Ingersoll, F., Brochu, P., Bharel, M., Skenazy, S., Karthikesalingam, A., Bavadekar, S., Kansal, M., Shekel, T., Gabrilovich, E. & Wellenius, G. A.
Lancet Planet Health 8, e564–e573 (2024).
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Publications
Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis.Banke-Thomas, A., Wong, K. L. M., Olubodun, T., Macharia, P. M., Sundararajan, N., Shah, Y., Prasad, G., Kansal, M., Vispute, S., Shekel, T., Ogunyemi, O., Gwacham-Anisiobi, U., Wang, J., Abejirinde, I.-O. O., Makanga, P. T., Azodoh, N., Nzelu, C., Afolabi, B. B., Stanton, C. & Beňová, L.
The Lancet Global Health 12, e848–e858 (2024).
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Publications
Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban NigeriaWong, K. L. M., Banke-Thomas, A., Olubodun, T., Macharia, P. M., Stanton, C., Sundararajan, N., Shah, Y., Prasad, G., Kansal, M., Vispute, S., Shekel, T., Ogunyemi, O., Gwacham-Anisiobi, U., Wang, J., Abejirinde, I.-O. O., Makanga, P. T., Afolabi, B. B. & Beňová, L.
Commun. Med. 4, 34 (2024).
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Publications
Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemicGligorić, K., Kamath, C., Weiss, D. J., Bavadekar, S., Liu, Y., Shekel, T., Schulman, K. & Gabrilovich, E.
Communications Medicine 3, 1–11 (2023).
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Publications
A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbationsMacharia, P. M., Wong, K. L. M., Olubodun, T., Beňová, L., Stanton, C., Sundararajan, N., Shah, Y., Prasad, G., Kansal, M., Vispute, S., Shekel, T., Gwacham-Anisiobi, U., Ogunyemi, O., Wang, J., Abejirinde, I.-O. O., Makanga, P. T., Afolabi, B. B. & Banke-Thomas, A.
Sci Data 10, 736 (2023).
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Publications
Comparing access to urban parks across six OECD countriesVeneri, P., Kaufmann, T., Vispute, S., Shekel, T., Gabrilovich, E., Wellenius, G. A., Dijkstra, L. & Kansal, M.
(Organisation for Economic Co-Operation and Development (OECD), 2023).
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Publications
Identifying COVID-19 Vaccine Deserts and Ways to Reduce Them: A Digital Tool to Support Public Health Decision-MakingWeintraub, R. L., Miller, K., Rader, B., Rosenberg, J., Srinath, S., Woodbury, S. R., Schultheiss, M. D., Kansal, M., Vispute, S., Serghiou, S., Flores, G., Kumok, A., Shekel, T., Gabrilovich, E., Ahmad, I., Chiang, M. E. & Brownstein, J. S.
Am. J. Public Health e1–e5 (2023).
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Publications
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search ClassificationGupta, J., Tay, Y., Kamath, C., Tran, V., Metzler, D., Bavadekar, S., Sun, M. & Gabrilovich, E.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 521–530 (2022).
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Publications
An evaluation of Internet searches as a marker of trends in population mental health in the USVaidyanathan, U., Sun, Y., Shekel, T., Chou, K., Galea, S., Gabrilovich, E. & Wellenius, G. A.
Sci. Rep. 12, 8946 (2022). [readcube]
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Publications
COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus diseaseWahltinez, O., Cheung, A., Alcantara, R., Cheung, D., Daswani, M., Erlinger, A., Lee, M., Yawalkar, P., Lê, P., Navarro, O. P., Brenner, M. P. & Murphy, K.
Sci Data 9, 162 (2022).
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Publications
Vaccine Search Patterns Provide Insights into Vaccination IntentMalahy, S., Sun, M., Spangler, K., Leibler, J., Lane, K., Bavadekar, S., Kamath, C., Kumok, A., Sun, Y., Gupta, J., Griffith, T., Boulanger, A., Young, M., Stanton, C., Mayer, Y., Smith, K., Shekel, T., Chou, K., Corrado, G., Levy, J., Szpiro, A., Gabrilovich, E. & Wellenius, G. A.
arXiv [cs.SI] (2021).
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Publications
Google COVID-19 Vaccination Search Insights: Anonymization Process DescriptionBavadekar, S., Boulanger, A., Davis, J., Desfontaines, D., Gabrilovich, E., Gadepalli, K., Ghazi, B., Griffith, T., Gupta, J., Kamath, C., Kraft, D., Kumar, R., Kumok, A., Mayer, Y., Manurangsi, P., Patankar, A., Perera, I. M., Scott, C., Shekel, T., Miller, B., Smith, K., Stanton, C., Sun, M., Young, M. & Wellenius, G.
arXiv [cs.CR] (2021).
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Publications
Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020Woskie, L. R., Hennessy, J., Espinosa, V., Tsai, T. C., Vispute, S., Jacobson, B. H., Cattuto, C., Gauvin, L., Tizzoni, M., Fabrikant, A., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Stanton, C., Bavadekar, S., Abueg, M., Hogue, M., Oplinger, A., Chou, K., Corrado, G., Shekel, T., Jha, A. K., Wellenius, G. A. & Gabrilovich, E.
PLoS One 16, e0253071 (2021).
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Publications
Impacts of social distancing policies on mobility and COVID-19 case growth in the USWellenius, G. A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T. C., Hennessy, J., Dai, A., Williams, B., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Bendebury, C., Mandayam, C., Stanton, C., Bavadekar, S., Pluntke, C., Desfontaines, D., Jacobson, B. H., Armstrong, Z., Gipson, B., Wilson, R., Widdowson, A., Chou, K., Oplinger, A., Shekel, T., Jha, A. K. & Gabrilovich, E.
Nat. Commun. 12, 3118 (2021).
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Publications
Forecasting influenza activity using machine-learned mobility mapVenkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C. L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B., Higdon, D., Kucuktunc, O., Lieber, A., Lewis, B. L., Reynolds, Z., Vullikanti, A. K., Wang, L. & Marathe, M.
Nat. Commun. 12, 726 (2021).
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Publications
Global maps of travel time to healthcare facilitiesWeiss, D. J., Nelson, A., Vargas-Ruiz, C. A., Gligorić, K., Bavadekar, S., Gabrilovich, E., Bertozzi-Villa, A., Rozier, J., Gibson, H. S., Shekel, T., Kamath, C., Lieber, A., Schulman, K., Shao, Y., Qarkaxhija, V., Nandi, A. K., Keddie, S. H., Rumisha, S., Amratia, P., Arambepola, R., Chestnutt, E. G., Millar, J. J., Symons, T. L., Cameron, E., Battle, K. E., Bhatt, S. & Gething, P. W.
Nat. Med. (2020).
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Publications
Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington stateAbueg, M., Hinch, R., Wu, N., Liu, L., Probert, W. J. M., Wu, A., Eastham, P., Shafi, Y., Rosencrantz, M., Dikovsky, M., Cheng, Z., Nurtay, A., Abeler-Dörner, L., Bonsall, D. G., McConnell, M. V., O’Banion, S. & Fraser, C.
medRxiv (2020). doi:10.1101/2020.08.29.20184135
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Publications
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)Bavadekar, S., Dai, A., Davis, J., Desfontaines, D., Eckstein, I., Everett, K., Fabrikant, A., Flores, G., Gabrilovich, E., Gadepalli, K., Glass, S., Huang, R., Kamath, C., Kraft, D., Kumok, A., Marfatia, H., Mayer, Y., Miller, B., Pearce, A., Perera, I. M., Ramachandran, V., Raman, K., Roessler, T., Shafran, I., Shekel, T., Stanton, C., Stimes, J., Sun, M., Wellenius, G. & Zoghi, M.
arXiv [cs.CR] (2020).
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Publications
Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 PandemicWellenius, G. A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T. C., Hennessy, J., Williams, B., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Bendebury, C., Stanton, C., Bavadekar, S., Pluntke, C., Desfontaines, D., Jacobson, B., Armstrong, Z., Gipson, B., Wilson, R., Widdowson, A., Chou, K., Oplinger, A., Shekel, T., Jha, A. K. & Gabrilovich, E.
arXiv [q-bio.PE] (2020).
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Publications
Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0)Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., Gabrilovich, E., Gadepalli, K., Gipson, B., Guevara, M., Kamath, C., Kansal, M., Lange, A., Mandayam, C., Oplinger, A., Pluntke, C., Roessler, T., Schlosberg, A., Shekel, T., Vispute, S., Vu, M., Wellenius, G., Williams, B. & Wilson, R. J.
arXiv [cs.CR] (2020).
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Publications
Assessing the impact of coordinated COVID-19 exit strategies across EuropeRuktanonchai, N. W., Floyd, J. R., Lai, S., Ruktanonchai, C. W., Sadilek, A., Rente-Lourenco, P., Ben, X., Carioli, A., Gwinn, J., Steele, J. E., Prosper, O., Schneider, A., Oplinger, A., Eastham, P. & Tatem, A. J.
Science 369, 1465–1470 (2020).
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Publications
Lymelight: forecasting Lyme disease risk using web search dataSadilek, A., Hswen, Y., Bavadekar, S., Shekel, T., Brownstein, J. S. & Gabrilovich, E.
npj Digital Medicine 3, 1–12 (2020).
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Publications
Hierarchical organization of urban mobility and its connection with city livabilityBassolas, A., Barbosa-Filho, H., Dickinson, B., Dotiwalla, X., Eastham, P., Gallotti, R., Ghoshal, G., Gipson, B., Hazarie, S. A., Kautz, H., Kucuktunc, O., Lieber, A., Sadilek, A., & Ramasco, J. J.
Nat. Commun. 10, 4817 (2019).
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Publications
Machine-learned epidemiology: real-time detection of foodborne illness at scaleSadilek, A., Caty, S., DiPrete, L., Mansour, R., Schenk Jr., T., Bergtholdt, M., Jha, A., Ramaswami P., & Gabrilovich E.
npj Digital Med 1, 36 (2018).
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Blog Posts
Taking medical imaging embeddings 3Dby Atilla Kiraly & Madeleine Traverse
Google Research Blog | 21-Oct-2024
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Blog Posts
Computer-aided diagnosis for lung cancer screeningby Atilla Kiraly & Rory Pilgrim
Google Research Blog | 20-Mar-2024
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Blog Posts
How AI supports early disease detection in Indiaby Shravya Shetty
Google Keyword Blog | 19-Mar-2024
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Blog Posts
How AI is helping advance women’s health around the worldGoogle Keyword Blog | 8-Mar-2024
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Blog Posts
5 ways Google is accelerating Health AI innovation in Africaby Yossi Matias & Shravya Shetty
Google Africa Blog | 31-Oct-2023
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Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
Google Keyword Blog | 18-Jul-2023
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Blog Posts
On-device fetal ultrasound assessment with TensorFlow Liteby Angelica Willis & Akib Uddin
TensorFlow Blog | 20-Jun-2023
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Blog Posts
6 ways Google is working with AI in Africaby Perry Nelson & Aisha Walcott-Bryant
Google Africa Blog | 1-Jun-2023
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Blog Posts
Our latest health AI research updatesby Greg Corrado & Yossi Matias
Google Keyword Blog | 14-Mar-2023
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
Google Keyword Blog | 17-Jan-2023
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Blog Posts
Partnering with iCAD to improve breast cancer screeningby Greg Corrado
Google Keyword Blog | 28-Nov-2022
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Blog Posts
How AI can help in the fight against breast cancerby Nicole Linton
Google Keyword Blog | 21-Oct-2022
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Blog Posts
Simplified Transfer Learning for Chest Radiography Model Developmentby Akib Uddin & Andrew Sellergren
Google Research Blog | 19-Jul-2022
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
Google Research Blog | 24-Mar-2022
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Blog Posts
Mammography collaboration in JapanGoogle Japan Blog | 25-Nov-2021
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Blog Posts
Detecting Abnormal Chest X-rays using Deep Learningby Zaid Nabulsi & Po-Hsuan Cameron Chen
Google Research Blog | 1-Sep-2021
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Blog Posts
Tackling tuberculosis screening with AIby Rory Pilgrim & Shruthi Prabhakara
Google Keyword Blog | 18-May-2021
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Blog Posts
Using artificial intelligence in breast cancer screeningby Sunny Jansen & Krish Eswaran
Google Keyword Blog | 25-Feb-2021
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Blog Posts
Exploring AI for radiotherapy planning with Mayo Clinicby Cian Hughes
Google Keyword Blog | 29-Oct-2020
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Blog Posts
Using AI to improve breast cancer screeningby Shravya Shetty & Daniel Tse
Google Keyword Blog | 1-Jan-2020
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Blog Posts
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labelsby Dave Steiner & Shravya Shetty
Google Research Blog | 3-Dec-2019
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Blog Posts
A promising step forward for predicting lung cancerby Shravya Shetty
Google Keyword Blog | 20-May-2019
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Publications
Prospective multi-site validation of AI to detect tuberculosis and chest X-ray abnormalitiesKazemzadeh, S., Kiraly, A. P., Nabulsi, Z., Sanjase, N., Maimbolwa, M., Shuma, B., Jamshy, S., Chen, C., Agharwal, A., T. Lau, C., Sellergren, A., Golden, D., Yu, J., Wu, E., Matias, Y., Chou, K., Corrado, G. S., Shetty, S., Tse, D., Eswaran, K., Liu, Y., Pilgrim, R., Muyoyeta, M. & Prabhakara, S.
NEJM AI 1, (2024). [Free access link]
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Publications
Artificial intelligence as a second reader for screening mammography.Nakai, E., Miyagi, Y., Suzuki, K., Scoccia Pappagallo, A., Kayama, H., Matsuba, T., Yang, L., Xu, S., Kelly, C., Najafi, R., Kohlberger, T., Golden, D., Uddin, A., Nakamura, Y., Kokubu, Y., Takahashi, Y., Ueno, T., Oguchi, M., Ohno, S. & Ledsam, J. R.
Radiology Advances 1, (2024).
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Publications
Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and JapanKiraly, A. P., Cunningham, C. A., Najafi, R., Nabulsi, Z., Yang, J., Lau, C., Ledsam, J. R., Ye, W., Ardila, D., McKinney, S. M., Pilgrim, R., Liu, Y., Saito, H., Shimamura, Y., Etemadi, M., Melnick, D., Jansen, S., Corrado, G. S., Peng, L., Tse, D., Shetty, S., Prabhakara, S., Naidich, D. P., Beladia, N. & Eswaran, K.
Radiol Artif Intell e230079 (2024).
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Publications
Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planningLucido, J. J., DeWees, T. A., Leavitt, T. R., Anand, A., Beltran, C. J., Brooke, M. D., Buroker, J. R., Foote, R. L., Foss, O. R., Gleason, A. M., Hodge, T. L., Hughes, C. O., Hunzeker, A. E., Laack, N. N., Lenz, T. K., Livne, M., Morigami, M., Moseley, D. J., Undahl, L. M., Patel, Y., Tryggestad, E. J., Walker, M. Z., Zverovitch, A. & Patel, S. H.
Front. Oncol. 13, (2023).
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Publications
Development of a Machine Learning Model for Sonographic Assessment of Gestational AgeLee, C., Willis, A., Chen, C., Sieniek, M., Watters, A., Stetson, B., Uddin, A., Wong, J., Pilgrim, R., Chou, K., Tse, D., Shetty, S. & Gomes, R. G.
JAMA Netw Open 6, e2248685 (2023).
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Publications
A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessmentGomes, R. G., Vwalika, B., Lee, C., Willis, A., Sieniek, M., Price, J. T., Chen, C., Kasaro, M. P., Taylor, J. A., Stringer, E. M., McKinney, S. M., Sindano, N., Dahl, G. E., Goodnight, W., Gilmer, J., Chi, B. H., Lau, C., Spitz, T., Saensuksopa, T., Liu, K., Tiyasirichokchai, T., Wong, J., Pilgrim, R., Uddin, A., Corrado, G., Peng, L., Chou, K., Tse, D., Stringer, J. S. A. & Shetty, S.
Communications Medicine 2, 1–9 (2022).
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Publications
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of RadiologistsKazemzadeh, S., Yu, J., Jamshy, S., Pilgrim, R., Nabulsi, Z., Chen, C., Beladia, N., Lau, C., McKinney, S. M., Hughes, T., Kiraly, A. P., Kalidindi, S. R., Muyoyeta, M., Malemela, J., Shih, T., Corrado, G. S., Peng, L., Chou, K., Chen, P.-H. C., Liu, Y., Eswaran, K., Tse, D., Shetty, S. & Prabhakara, S.
Radiology 212213 (2022).
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Publications
Simplified Transfer Learning for Chest Radiography Models Using Less DataSellergren, A. B., Chen, C., Nabulsi, Z., Li, Y., Maschinot, A., Sarna, A., Huang, J., Lau, C., Kalidindi, S. R., Etemadi, M., Garcia-Vicente, F., Melnick, D., Liu, Y., Eswaran, K., Tse, D., Beladia, N., Krishnan, D. & Shetty, S.
Radiology 212482 (2022).
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Publications
Study Design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planningAnand, A., Beltran, C. J., Brooke, M. D., Buroker, J. R., DeWees, T. A., Foote, R. L., Foss, O. R., Hughes, C. O., Hunzeker, A. E., John Lucido, J., Morigami, M., Moseley, D. J., Pafundi, D. H., Patel, S. H., Patel, Y., Ridgway, A. K., Tryggestad, E. J., Wilson, M. Z., Xi, L. & Zverovitch, A.
medRxiv 2021.12.07.21266421 (2021).
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Publications
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19Nabulsi, Z., Sellergren, A., Jamshy, S., Lau, C., Santos, E., Kiraly, A. P., Ye, W., Yang, J., Pilgrim, R., Kazemzadeh, S., Yu, J., Kalidindi, S. R., Etemadi, M., Garcia-Vicente, F., Melnick, D., Corrado, G. S., Peng, L., Eswaran, K., Tse, D., Beladia, N., Liu, Y., Chen, P.-H. C. & Shetty, S.
Sci. Rep. 11, 1–15 (2021).
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Publications
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation StudyNikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., Patel, Y., Meyer, C., Askham, H., Romera-Paredes, B., Kelly, C., Karthikesalingam, A., Chu, C., Carnell, D., Boon, C., D’Souza, D., Moinuddin, S. A., Garie, B., McQuinlan, Y., Ireland, S., Hampton, K., Fuller, K., Montgomery, H., Rees, G., Suleyman, M., Back, T., Hughes, C. O., Ledsam, J. R. & Ronneberger, O.
J. Med. Internet Res. 23, e26151 (2021).
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Publications
Improving reference standards for validation of AI-based radiographyDuggan, G. E., Reicher, J. J., Liu, Y., Tse, D. & Shetty, S.
Br J Radiol. 94, 20210435 (2021).
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Publications
International evaluation of an AI system for breast cancer screeningMcKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J. & Shetty, S.
Nature 577, 89–94 (2020). [readcube]
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Publications
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted EvaluationMajkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., Ding, A., Corrado, G. S., Tse, D. & Shetty, S.
Radiology 191293 (2019).
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Publications
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomographyArdila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reciher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., Shetty, S.
Nat. Med. 25, 954–961 (2019). [readcube]
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Blog Posts [more at Youtube Official Blog]
Exploring how AI tools can help increase high-quality health contentby Garth Graham & Viknesh Sounderajah
YouTube Official Blog | 23-Oct-2024
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Blog Posts [more at Youtube Official Blog]
How we’re using AI to connect people to health informationGoogle Keyword Blog | 19-Mar-2024
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Blog Posts
Safer Internet Day: Supporting teen mental health and wellbeing on YouTubeby The YouTube Team
Youtube Official Blog | 6-Feb-2024
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Blog Posts
Elevating first aid information on YouTube searchby Garth Graham
Youtube Official Blog | 10-Jan-2024
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Blog Posts
How AI helps make public health truly publicby Garth Graham
Youtube Official Blog | 14-Dec-2023
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Blog Posts
Continued support for teen wellbeing and mental health on YouTubeby James Beser
Youtube Official Blog | 2-Nov-2023
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Blog Posts
Expanding equitable access to health information on YouTubeby Garth Graham
Youtube Official Blog | 7-Sep-2023
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Blog Posts [more at Youtube Official Blog]
A long term vision for YouTube’s medical misinformation policiesYoutube Official Blog
15-Aug-2023
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Blog Posts
New ways for UK licensed healthcare professionals to reach viewers on YouTubeYoutube Official Blog
12-Jun-2023
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Blog Posts
Mental Health Action Day: Small steps to support your mental healthYoutube Official Blog
15-May-2023
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Blog Posts
An updated approach to eating disorder-related contentby Garth Graham
Youtube Official Blog | 18-Apr-2023
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Blog Posts
Finding connection and support this World Mental Health Dayby Jessica DiVento Dzuban
Youtube Official Blog | 7-Oct-2022
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Blog Posts
Expanding clinicians’ access to Continuing Educationby Garth Graham
Youtube Official Blog | 1-Mar-2023
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Blog Posts
8 things we launched in 2022 to support your healthby Iz Conroy
Google Keyword Blog | 21-Dec-2022
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Blog Posts
New ways for licensed healthcare professionals to reach people on YouTubeby Garth Graham
Youtube Official Blog | 27-Oct-2022
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Blog Posts
Answering the human questions: How we’re putting patient voices front and centerby Garth Graham
Youtube Official Blog | 28–Sep-2022
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Blog Posts
Our work toward health equityby Ivor Horn
Google Keyword Blog | 12-Sep-2022
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Blog Posts
Introducing THE-IQ: tackling health equity with YouTube Health and Kaiser Family Foundationby Garth Graham
Youtube Official Blog | 12–Sep-2022
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Blog Posts
New ways to answer your health questions in the United Kingdomby Garth Graham
Youtube Official Blog | 15-Jun-2022
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Blog Posts
The Check Up: helping people live healthier livesby Karen DeSalvo
Google Keyword Blog | 24-Mar-2022
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Blog Posts
Answering your health questions in Brazil, India, and Japanby Garth Graham
Youtube Official Blog | 24-Mar-2022
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Blog Posts
Access to information is a health equity issue. Here’s how YouTube is helping make high quality health information available to everyoneby Garth Graham
Youtube Official Blog | 26-Jan-2022
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Blog Posts
Doctors bring their expertise on vaccines to YouTubeby Garth Graham
Youtube Official Blog | 13-Oct-2021
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Blog Posts
Introducing new ways to help you find answers to your health questionsby Garth Graham
Youtube Official Blog | 19-Jul-2021