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]
How we’re using AI to connect people to health informationby Karen DeSalvo
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Blog Posts
How AI is helping advance women’s health around the worldby Ronit Levavi Morad & Preeti Singh
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Blog Posts
A new commitment to digital wellbeing for kids and teensby Karen DeSalvo
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Blog Posts
3 predictions for AI in healthcare in 2024by Aashima Gupta
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Blog Posts
2023: A year of groundbreaking advances in AI and computingby Jeff Dean, James Manyika, & Demis Hassabis
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Blog Posts
23 of our biggest moments in 2023by Molly McHugh-Johnson
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Blog Posts
4 ways we think about health equity and AIby Ivor Horn
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Blog Posts
5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia Shravya Shetty
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Blog Posts
How we’re using AI to help transform healthcareby Yossi Mattias
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Blog Posts
A new collaboration to improve nutrition informationby Nira Goren
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Blog Posts
HLTH 2023: Bringing AI to health responsiblyby Michaell Howell
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Blog Posts
How AI can improve health for everyone, everywhereby Karen DeSalvo
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Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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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
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Blog Posts
New research from the UK focused on technology’s role in healthcareby Susan Thomas
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Blog Posts
Our collaboration with WHO to improve public healthby Karen DeSalvo
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Blog Posts
Partnering with startups using AI to improve healthcareby Karen DeSalvo
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Blog Posts
More mental health resources for the moments you need themby Megan Jones Bell
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Blog Posts
3 ways Google products can help you feel less stressedby Megan Jones Bell
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Blog Posts
New ways we’re helping people live healthier livesby Karen DeSalvo
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Blog Posts
Our latest health AI research updatesby Greg Corrado & Yossi Matias
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Blog Posts
Google Research, 2022 & beyond: Healthby Greg Corrado & Yossi Matias
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Blog Posts
Meet our Health Equity Research Initiative awardeesby Ivor Horn
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
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Blog Posts
3 ways to take better care of your mind and body in 2023by Megan Jones Bell
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Blog Posts
8 things we launched in 2022 to support your healthby Iz Conroy
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Blog Posts
How to use Google Search to help manage uncertain timesby Hema Budaraju
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Blog Posts
Unlocking the potential of technology to support healthby Karen DeSalvo
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Blog Posts
Healthy collaboration: Why partnerships are the heart of healthcare innovationby Aashima Gupta
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Blog Posts
3 ways AI is scaling helpful technologies worldwideby Jeff Dean
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Blog Posts
Democratizing access to healthby Karen DeSalvo
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Blog Posts
Google Assistant offers information and hope for Breast Cancer Awareness Monthby Riva Sciuto
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Blog Posts
Our work toward health equityby Ivor Horn
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Blog Posts
Dr. Von Nguyen’s temperature check on public healthby Lauren Winer
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Blog Posts
Suicide prevention resources on Google Searchby Anne Merritt
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Blog Posts
Mental health resources you can count onby Megan Jones Bell
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Blog Posts
Raising awareness of the dangers of fentanylby Megan Jones Bell & Garth Graham
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Blog Posts
The Check Up: helping people live healthier livesby Karen DeSalvo
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
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Blog Posts
Extending Care Studio with a new healthcare partnershipby Paul Muret
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Blog Posts
Take a look at Conditions, our new feature in Care Studioby Paul Muret
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Blog Posts
Google Research: Themes from 2021 and Beyondby Jeff Dean
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Blog Posts
Making healthcare options more accessible on Searchby Hema Budaraju
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Blog Posts
HLTH: Building on our commitments in healthby Karen DeSalvo
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Blog Posts
When it comes to mental health, what are we searching for?by Alicia Cormie
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Blog Posts
Dr. Ivor Horn talks about technology and health equityby Alicia Cormie
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Blog Posts
Our Care Studio pilot is expanding to more cliniciansby Paul Muret
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Blog Posts
Google Research: Looking Back at 2020, and Forward to 2021by Jeff Dean
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Blog Posts
A new Google Search tool to support women with postpartum depressionby David Feinberg
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Blog Posts
Prepare for medical visits with help from Google and AHRQby Dave Greenwood
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Blog Posts
A Collaborative Approach to Shaping Successful UX Critique Practicesby Anna Lurchenko
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Blog Posts
Learn more about anxiety with a self-assessment on Searchby Daniel Gillison, Jr
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Blog Posts
Google Research: Looking Back at 2019, and Forward to 2020 and Beyondby Jeff Dean
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Blog Posts
Lessons Learned from Developing ML for Healthcareby Yun Liu & Po-Hsuan Cameron Chen
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Blog Posts
Tools to help healthcare providers deliver better careby David Feinberg
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Blog Posts
Breast cancer and tech...a reason for optimismby Ruth Porat
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Blog Posts
DeepMind’s health team joins Google Healthby Dominic King
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Blog Posts
Looking Back at Google’s Research Efforts in 2018by Jeff Dean
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Blog Posts
Meet David Feinberg, head of Google Healthby Google
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Blog Posts
AI for Social Good in Asia Pacificby Kent Walter
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Blog Posts
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)by Jeff Dean
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Blog Posts
Gain a deeper understanding of Posttraumatic Stress Disorder on Googleby Paula Schnurr & Teri Brister
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Blog Posts
Learning more about clinical depression with the PHQ-9 questionnaireby Mary Giliberti
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Blog Posts
Partnering on machine learning in healthcareby Katherine Chou
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Blog Posts
The Google Brain Team — Looking Back on 2016by Jeff Dean
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COVID-19 Blog Posts
Supporting evolving COVID information needsby Hema Budaraju
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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
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COVID-19 Blog Posts [more at Google Keyword Blog]
This year, we searched for ways to stay healthyby Hema Budaraju
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COVID-19 Blog Posts
New tools to support vaccine access and distributionby Tomer Shekel
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COVID-19 Blog Posts
An update on our COVID response prioritiesby the COVID Response team, Google India
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COVID-19 Blog Posts
Our commitment to COVID-19 vaccine equityby Karen DeSalvo
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COVID-19 Blog Posts
How anonymized data helps fight against diseaseby Stephen Ratcliffe
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COVID-19 Blog Posts
How we’re helping get vaccines to more peopleby Sundar Pichai
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COVID-19 Blog Posts
Exposure Notifications: end of year updateby Steph Hannon
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COVID-19 Blog Posts
How you'll find accurate and timely information on COVID-19 vaccinesby Karen DeSalvo & Kristie Canegallo
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COVID-19 Blog Posts
How I’m giving thanks (and staying safe) this Thanksgivingby Karen DeSalvo
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COVID-19 Blog Posts
A Q&A on coronavirus vaccinesGoogle Keyword Blog
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COVID-19 Blog Posts
An update on our efforts to help Americans navigate COVID-19by Ruth Porat
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COVID-19 Blog Posts
Making data useful for public healthby Katherine Chou
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COVID-19 Blog Posts
Google supports COVID-19 AI and data analytics projectsby Mollie Javerbaum & Meghan Houghton
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COVID-19 Blog Posts
Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
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COVID-19 Blog Posts
An update on Exposure Notificationsby Dave Burke
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COVID-19 Blog Posts
Exposure Notification API launches to support public health agenciesby Apple & Google
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COVID-19 Blog Posts
Dr. Karen DeSalvo on ‘putting information first’ during COVID-19by Megan Washam
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COVID-19 Blog Posts
Resources for mental health support during COVID-19by David Feinberg
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COVID-19 Blog Posts
Helping you avoid COVID-19 online security risksGoogle Africa Blog
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COVID-19 Blog Posts
Apple and Google partner on COVID-19 contact tracing technologyby Apple & Google
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COVID-19 Blog Posts
Connecting people to virtual care optionsby Julie Black
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COVID-19 Blog Posts
Support for public health workers fighting COVID-19by Karen DeSalvo
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COVID-19 Blog Posts
Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
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COVID-19 Blog Posts
Connecting people with COVID-19 information and resourcesby Emily Moxley
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COVID-19 Blog Posts
COVID-19: How we’re continuing to helpby Sundar Pichai
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COVID-19 Blog Posts
Coronavirus: How we’re helpingby Sundar Pichai
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Reviews
The opportunities and risks of large language models in mental healthLawrence, H. R., Schneider, R. A., Rubin, S. B., Mataric, M. J., McDuff, D. J. & Bell, M. J.
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Reviews
Information is a determinant of healthGraham, G., Goren, N., Sounderajah, V. & DeSalvo, K.
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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.
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Reviews
Three Epochs of Artificial Intelligence in Health CareHowell M., Corrado G., DeSalvo K.
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Reviews
Artificial intelligence in healthcare: a perspective from GoogleLehmann, L. S., Natarajan, V. & Peng, L. Chapter 39
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Reviews
Explaining counterfactual imagesLang, O., Traynis, I. & Liu, Y.
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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
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Reviews
Deep Learning for Epidemiologists: An introduction to neural networks.Serghiou, S. & Rough, K.
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Blog Posts
Building a Clinical Team in a Large Technology Company.DeSalvo Karen B. & Howell Michael D.
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Reviews
Medicine’s Role in Reimagining Public Health: Reuniting Panacea and HygeiaDeSalvo, K. B., Kadakia, K. T. & Chokshi, D. A.
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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.
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Reviews
Public Health 3.0 After COVID-19-Reboot or Upgrade?DeSalvo, K. B. & Kadakia, K. T.
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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.
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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.
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Reviews
Artificial Intelligence in MedicineKelly, C. J., Brown, A. P. Y. & Taylor, J. A.
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Reviews
Challenges of Accuracy in Germline Clinical Sequencing DataPoplin, R., Zook, J. M. & DePristo, M.
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Reviews
Retinal detection of kidney disease and diabetesMitani, A., Hammel, N. & Liu, Y.
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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.
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Reviews
Closing the translation gap: AI applications in digital pathologySteiner, D. F., Chen, P.-H. C. & Mermel, C. H.
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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.
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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.
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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.
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Reviews
Artificial intelligence, machine learning and deep learning for eye care specialistsSayres, R., Hammel, N. & Liu, Y.
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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.
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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.
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Reviews
Key challenges for delivering clinical impact with artificial intelligenceKelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.
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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.
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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.
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Reviews
How to develop machine learning models for healthcareChen, C. P.-H., Liu, Y., & Peng, L.
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Reviews
Machine Learning in MedicineRajkomar, A., Dean, J., & Kohane I.
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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.
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Reviews
When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ dataRough K, Thompson J.
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Reviews
Resolving the Productivity Paradox of Health Information Technology: A Time for OptimismWachter, R. M., Howell, M. D.
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Blog Posts
Developing reliable AI tools for healthcareby Krishnamurthy (Dj) Dvijotham & Taylan Cemgil
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Blog Posts
Robust and efficient medical imaging with self-supervisionby Shekoofeh Azizi & Laura Culp
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Blog Posts
How Underspecification Presents Challenges for Machine Learningby Alex D’Amour & Katherine Heller
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Blog Posts
Self-Supervised Learning Advances Medical Image Classificationby Shekoofeh Azizi
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Publications
Supervised Transfer Learning at Scale for Medical ImagingMustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Karthikesalingam, A., Houlsby, N. & Natarajan, V.
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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.
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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.
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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.
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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.
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Blog Posts
How we’re using AI to connect people to health information -
Blog Posts
3 ways we are building equity into our health workby Ivor Horn
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Blog Posts
SCIN: A new resource for representative dermatology imagesby Pooja Rao
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Blog Posts
HEAL: A framework for health equity assessment of machine learning performanceby Mike Schaekermann & Ivor Horn
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Blog Posts
Health-specific embedding tools for dermatology and pathologyby Dave Steiner & Rory Pilgrim
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Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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Blog Posts
8 ways Google Lens can help make your life easierby Lou Wang
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Blog Posts
Ask a Techspert: What does AI do when it doesn’t know?by Iz Conroy
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Blog Posts
Does Your Medical Image Classifier Know What It Doesn’t Know?by Abhijit Guha Roy & Jie Ren
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Blog Posts
How DermAssist uses TensorFlow.js for on-device image quality checksby Miles Hutson & Aaron Loh
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Blog Posts
Using AI to help find answers to common skin conditionsby Peggy Bui & Yuan Liu
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Blog Posts
AI assists doctors in interpreting skin conditionsby Ayush Jain & Peggy Bui
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Blog Posts
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
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Blog Posts
Using Deep Learning to Inform Differential Diagnoses of Skin Diseasesby Yuan Liu & Peggy Bui
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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.
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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.
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Publications
A Reduction to Binary Approach for Debiasing Multiclass Datasets. Advances in Neural Information Processing SystemsAlabdulmohsin, I.M., Schrouff, J., Koyejo, S.
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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.
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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.
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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.
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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.
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Publications
Addressing the Real-world Class Imbalance Problem in DermatologyWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.
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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.
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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.
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Publications
DermGAN: Synthetic Generation of Clinical Skin Images with PathologyGhorbani, A., Natarajan, V., Coz, D. & Liu, Y.
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Publications
Measuring clinician-machine agreement in differential diagnoses for dermatologyEng, C., Liu, Y. & Bhatnagar, R.
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Blog Posts
Improved Detection of Elusive Polyps via Machine Learningby Yossi Matias & Ehud Rivlin
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Blog Posts
Verily Opens New R&D Center in Israel Focused on the Application of AI in Healthcareby Robin Suchan
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Blog Posts
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screeningsby Daniel Freedman & Ehud Rivlin
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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.
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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.
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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.
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Blog Posts
An ML-Based Framework for COVID-19 Epidemiologyby Joel Shor & Sercan Arik
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Google Cloud, Harvard Global Health Institute release improved COVID-19 Public Forecasts, share lessons learnedby Tomas Pfister
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Blog Posts
Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID-19 forecasting modelby Dario Sava
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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.
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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.
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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.
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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.
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Publications
Interpretable Sequence Learning for Covid-19 ForecastingArik, Li, Yoon, Sinha, Epshteyn, Le, Menon, Singh, Zhang, Nikoltchev, Sonthalia, Nakhost, Kanal & Pfister.
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Blog Posts
Google at 25: By the numbersby Michelle Budzyna & Molly McHugh-Johnson
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7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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5 myths about medical AI, debunkedby Kasumi Widner
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An eye to the future: How AI could help to improve detection of eye disease in Australian communitiesby Angus Turner
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Healthcare AI systems that put people at the centerby Emma Beede
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
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Blog Posts
New milestones in helping prevent eye disease with Verilyby Kasumi Widner & Sunny Virmani
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Blog Posts
Launching a powerful new screening tool for diabetic eye disease in India -
Blog Posts
AI for Social Good in Asia Pacificby Kent Walter
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Blog Posts
Improving the Effectiveness of Diabetic Retinopathy Modelsby Rory Sayres & Jonathan Krause
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Blog Posts
A major milestone for the treatment of eye diseaseby Mustafa Suleyman
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Blog Posts
Detecting diabetic eye disease with machine learningby Lily Peng
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Blog Posts
Deep learning for Detection of Diabetic Eye Diseaseby Lily Peng & Varun Gulshan
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Publications
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image AssessmentSchaekermann, M., Cai, C. J., Huang, A. E. & Sayres, R.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Publications
Who Said What: Modeling Individual Labelers Improves ClassificationGuan, M., Gulshan, V., Dai, A, Hinton, G.
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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.
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Blog Posts [more at Fitbit Blog]
How we’re using AI to connect people to health information -
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3 heart-health tips from Fitbit’s lead cardiologistby Molly McHugh-Johnson
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Blog Posts
6 things I learned after using the Fitbit Charge 6 for a weekby Mike Darling
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Blog Posts
New Fitbit study explores metabolic healthby Javier L. Prieto
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Blog Posts [more at Fitbit Blog]
3 ways Fitbit can improve your health — backed by researchby Amy McDonough
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How Google Pixel Watch 2 and Fitbit Charge 6 improved heart rate trackingby Molly McHugh-Johnson
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Google Pixel Watch 2: New ways to stay healthy, connected and safeby Sandeep Waraich
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Introducing Fitbit Charge 6: Our most advanced tracker yetby TJ Varghese
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Meet the new Fitbit app that’s redesigned with you in mindby Maggie Stanphill & Bhanu Narasimhan
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How we trained Fitbit’s Body Response feature to detect stressby Elena Perez & Samy Abdel-Ghaffer
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7 ways to stress less with Fitbitby Elena Perez
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3 ways Google products can help you feel less stressedby Megan Jones Bell
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6 ways Google AI is helping you sleep betterby Molly McHugh-Johnson
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3 ways to take better care of your mind and body in 2023by Megan Jones Bell
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8 things we launched in 2022 to support your healthby Iz Conroy
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I tried Fitbit’s new sleep features for two monthsby Zahra Barnes
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Google Pixel Watch: Help by Google, health by Fitbitby Sandeep Waraich
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8 things to try now on Fitbit Sense 2 and Versa 4by TJ Varghese
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Our work toward health equityby Ivor Horn
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Fitbit’s fall lineup: helping you live your healthiest lifeby TJ Varghese
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Kick-start your fitness routine with Fitbit Inspire 3by The Fitbit Team
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Manage your health and fitness with Fitbit Versa 4 and Sense 2by The Fitbit Team
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Improve your ZZZs with Fitbit Premium Sleep Profileby The Fitbit Team
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Mental health resources you can count onby Megan Jones Bell
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New Fitbit feature makes AFib detection more accessibleby The Fitbit Team
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Blog Posts
The Check Up: helping people live healthier livesby Karen DeSalvo
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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.
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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.
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Publications
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19Natarajan, A., Su, H.-W. & Heneghan, C.
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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.
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Blog Posts [more at DeepVariant Blog]
A breakthrough to better represent human genetic diversityby Andrew Carroll
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Blog Posts
Building better pangenomes to improve the equity of genomicsby Andrew Carroll & Kishwar Shafin
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Blog Posts
An ML-based approach to better characterize lung diseasesBabak Behsaz & Andrew Carroll
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Blog Posts
Developing an aging clock using deep learning on retinal imagesby Sara Ahadi & Andrew Carroll
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
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Blog Posts
A new genome sequencing tool powered with our technologyby Andrew Carroll
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Blog Posts
Advancing genomics to better understand and treat diseaseby Andrew Carroll & Pi-Chuan Chang
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Blog Posts
DeepNull: an open-source method to improve the discovery power of genetic association studiesby Farhad Hormozdiari & Andrew Carroll
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Blog Posts
Improving Genomic Discovery with Machine Learningby Andrew Carroll & Cory McLean
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Blog Posts
Improving the Accuracy of Genomic Analysis with DeepVariant 1.0by Andrew Carroll & Pi-Chuan Chang
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Blog Posts
DeepVariant Accuracy Improvements for Genetic Datatypesby Pi-Chuan Chang & Lizzie Dorfman
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Blog Posts
DeepVariant: Highly Accurate Genomes With Deep Neural Networksby Mark DePristo & Ryan Poplin
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Blog Posts
An AI Resident at work: Suhani Vora and her work on genomicsby Phing Lee
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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.
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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.
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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.
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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.
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Best: A Tool for Characterizing Sequencing ErrorsLiu, D., Belyaeva, A., Shafin, K., Chang, P.-C., Carroll, A. & Cook, D. E.
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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.
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An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic DiscoveryYuan, B., McLean, C. Y., Hormozdiari, F. I. & Cosentino, J.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Publications
GenomeWarp: an alignment-based variant coordinate transformationMcLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.
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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.
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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.
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Deep learning of genomic variation and regulatory network dataTelenti, A., Lippert, C., Chang, P.-C. & DePristo, M.
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Sequential regulatory activity prediction across chromosomes with convolutional neural networksKelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y. & Snoek, J.
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Expanding research on digital wellbeingby Nicholas Allen
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Advancing health research with Google Health Studiesby Jon Morgan & Paul Eastham
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How we built and tested body temperature on Pixel 8 Proby Molly McHugh-Johnson
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New Pixel features for a minty fresh start to the yearby Stephanie Scott
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Audioplethysmography for cardiac monitoring with hearable devicesby Xiaoran "Van" Fan & Trausti Thormundsson
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The Check Up: our latest health AI developmentsby Greg Corrado
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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.
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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.,
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3 ways we are building equity into our health workby Dr. Ivor Horn
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How 3 healthcare organizations are using generative AIby Aashima Gupta & Greg Corrado
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Our latest health AI research updatesby Greg Corrado & Yossi Matias
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Google Research, 2022 & beyond: Healthby Greg Corrado & Yossi Matias
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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.
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Towards Generalist Biomedical AI.Tu, 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.
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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.
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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.
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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.
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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.
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The Capability of Large Language Models to Measure Psychiatric FunctioningGalatzer-Levy, I. R., McDuff, D., Natarajan, V., Karthikesalingam, A. & Malgaroli, M.
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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.
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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.
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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.
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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.
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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.
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Joint Speech Recognition and Speaker Diarization via Sequence Transductionby Laurent El Shafey and Izhak Shafran
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How AI can improve products for people with impaired speechby Julie Cattiau
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Understanding Medical Conversationsby Katherine Chou and Chung-Cheng Chiu
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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.
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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.
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Extracting Symptoms and their Status from Clinical ConversationsDu, N., Chen, K., Kannan, A., Tran, L., Chen, Y. & Shafran, I.
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Automatically Charting Symptoms From Patient-Physician Conversations Using Machine LearningRajkomar, A., Kannan, A., Chen, K., Vardoulakis, L., Chou, K., Cui, C., & Dean, J.
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Joint Speech Recognition and Speaker Diarization via Sequence TransductionEl Shafey, L., Soltau, H. & Shafran, I.
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Learning to Infer Entities, Properties and their Relations from Clinical ConversationsDu, N., Wang, M., Tran, L., Li, G. & Shafran, I.
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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.
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Deciphering clinical abbreviations with privacy protecting MLby Alvin Rajkoma and Eric Loreaux
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EHR-Safe: Generating High-Fidelity and Privacy-Preserving Synthetic Electronic Health Recordsby Jinsung Yoon and Sercan O. Arik
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Multi-task Prediction of Organ Dysfunction in ICUsby Subhrajit Roy & Diana Mincu
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A Step Towards Protecting Patients from Medication Errorsby Kathryn Rough & Alvin Rajkomar
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Expanding the Application of Deep Learning to Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
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Scaling Streams with Googleby Demis Hassabis & Mustafa Suleyman & Dominic King
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Deep Learning for Electronic Health Recordsby Alvin Rajkomar & Eyal Oren
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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.
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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.
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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.
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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.
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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.
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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.
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Learning to Select Best Forecast Tasks for Clinical Outcome PredictionXue Y, Du N, Mottram A, Seneviratne A, Dai AM.
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Deep State-Space Generative Model For Correlated Time-to-Event PredictionsXue Y, Zhou D, Du N, Dai A, Xu Z, Zhang K, Cui C.
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Graph convolutional transformer: Learning the graphical structure of electronic health recordsChoi E, Xu Z, Li Y, Dusenberry MW, Flores G, Xue Y, Dai AM.
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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.
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Explaining an increase in predicted risk for clinical alertsHardt M, Rajkomar A, Flores G, Dai A, Howell M, Corrado G, Cui C, & Hardt M.
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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.
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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.
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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.
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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.
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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.
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Improved Patient Classification with Language Model Pretraining Over Clinical NotesKemp J, Rajkomar A, & Dai AM.
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Federated and Differentially Private Learning for Electronic Health RecordsPfohl SR, Dai AM, & Heller K.
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Deep Physiological State Space Model for Clinical ForecastingXue Y, Zhou D, Du N, Dai AM, Xu Z, Zhang K,& Cui C.
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Modelling EHR timeseries by restricting feature interactionZhang K, Xue Y, Flores G, Rajkomar A, Cui C, & Dai AM.
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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.
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Detecting novel systemic biomarkers in external eye photoby Boris Babenko & Akib Uddin
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Detecting Signs of Disease from External Images of the Eyeby Boris Babenko & Naama Hammel
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How AI could predict sight-threatening eye conditionsby Terry Spitz & Jim Winkens
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Using AI to predict retinal disease progressionby Jason Yim, Reena Chopra, Jeffrey De Fauw & Joseph Ledsam
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Detecting hidden signs of anemia from the eyeby Akinori Mitani
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Assessing Cardiovascular Risk Factors with Computer Visionby Lily Peng
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Collaborative Momentum: The 2023 State of the Digital Public Goods Ecosystem Report
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Health-specific embedding tools for dermatology and pathologyby Dave Steiner & Rory Pilgrim
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Using AI to Predict the Presence of Cancer Spreadby Justin Krogue, Yun Liu, Po-Hsuan Cameron Chen & Ellery A
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Learning from deep learning: a case study of feature discovery and validation in pathologyby Ellery Wulczyn and Yun Liu
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An International Scientific Challenge for the Diagnosis and Gleason Grading of Prostate Cancerby Po-Hsuan Cameron Chen & Maggie Demkin
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The promise of using AI to help prostate cancer careby Po-Hsuan Cameron Chen & Yun Liu
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PAIR @ CHI 2021by People + AI Research
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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
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Using AI to identify the aggressiveness of prostate cancerby Kunal Nagpal & Craig Mermel
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Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
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Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathologyby Narayan Hedge & Carrie Cai
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Improved Grading of Prostate Cancer Using Deep Learningby Martin Stumpe & Craig Mermel
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Applying Deep Learning to Metastatic Breast Cancer Detectionby Martin Stumpe & Craig Mermel
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An Augmented Reality Microscope for Cancer Detectionby Martin Stumpe & Craig Mermel
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Assisting Pathologists in Detecting Cancer with Deep Learningby Martin Stumpe & Lily Peng
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Onboarding Materials as Boundary Objects for Developing AI AssistantsCai, C.J., Steiner, D., Wilcox, L., Terry, M. and Winter, S.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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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.
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"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.
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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.
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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.
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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.
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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.
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How AI is helping advance women’s health around the world -
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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
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5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia & Shravya Shetty
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How we’re supporting access to emergency maternal care in Nigeriaby Charlotte Stanton
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An update on our efforts to help Americans navigate COVID-19by Ruth Porat
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Making data useful for public healthby Katherine Chou
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Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
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Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
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New Insights into Human Mobility with Privacy Preserving Aggregationby Adam Sadilek & Xerxes Dotiwalla
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Blog Posts
Computer-aided diagnosis for lung cancer screeningby Atilla Kiraly & Rory Pilgrim
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Blog Posts
How AI supports early disease detection in Indiaby Shravya Shetty
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Blog Posts
How AI is helping advance women’s health around the world -
Blog Posts
5 ways Google is accelerating Health AI innovation in Africaby Yossi Matias & Shravya Shetty
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Blog Posts
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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Blog Posts
On-device fetal ultrasound assessment with TensorFlow Liteby Angelica Willis & Akib Uddin
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Blog Posts
6 ways Google is working with AI in Africaby Perry Nelson & Aisha Walcott-Bryant
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Blog Posts
Our latest health AI research updatesby Greg Corrado & Yossi Matias
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Blog Posts
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
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Blog Posts
Partnering with iCAD to improve breast cancer screeningby Greg Corrado
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Blog Posts
How AI can help in the fight against breast cancerby Nicole Linton
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Blog Posts
Simplified Transfer Learning for Chest Radiography Model Developmentby Akib Uddin & Andrew Sellergren
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Blog Posts
The Check Up: our latest health AI developmentsby Greg Corrado
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Blog Posts
Mammography collaboration in Japan -
Blog Posts
Detecting Abnormal Chest X-rays using Deep Learningby Zaid Nabulsi & Po-Hsuan Cameron Chen
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Blog Posts
Tackling tuberculosis screening with AIby Rory Pilgrim & Shruthi Prabhakara
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Blog Posts
Using artificial intelligence in breast cancer screeningby Sunny Jansen & Krish Eswaran
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Blog Posts
Exploring AI for radiotherapy planning with Mayo Clinicby Cian Hughes
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Blog Posts
Using AI to improve breast cancer screeningby Shravya Shetty & Daniel Tse
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Blog Posts
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labelsby Dave Steiner & Shravya Shetty
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Blog Posts
A promising step forward for predicting lung cancerby Shravya Shetty
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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Blog Posts
Elevating first aid information on YouTube searchby Garth Graham
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How AI helps make public health truly publicby Garth Graham
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Blog Posts
Continued support for teen wellbeing and mental health on YouTubeby James Beser
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Blog Posts
Expanding equitable access to health information on YouTubeby Garth Graham
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Blog Posts [more at Youtube Official Blog]
A long term vision for YouTube’s medical misinformation policiesYoutube Official Blog
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New ways for UK licensed healthcare professionals to reach viewers on YouTubeYoutube Official Blog
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Mental Health Action Day: Small steps to support your mental healthYoutube Official Blog
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An updated approach to eating disorder-related contentby Garth Graham
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Finding connection and support this World Mental Health Dayby Jessica DiVento Dzuban
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Expanding clinicians’ access to Continuing Educationby Garth Graham
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8 things we launched in 2022 to support your healthby Iz Conroy
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New ways for licensed healthcare professionals to reach people on YouTubeby Garth Graham
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Answering the human questions: How we’re putting patient voices front and centerby Garth Graham
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Our work toward health equityby Ivor Horn
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Introducing THE-IQ: tackling health equity with YouTube Health and Kaiser Family Foundationby Garth Graham
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Blog Posts
New ways to answer your health questions in the United Kingdomby Garth Graham
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The Check Up: helping people live healthier livesby Karen DeSalvo
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Blog Posts
Answering your health questions in Brazil, India, and Japanby Garth Graham
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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
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Doctors bring their expertise on vaccines to YouTubeby Garth Graham
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Blog Posts
Introducing new ways to help you find answers to your health questionsby Garth Graham