Publicações de pesquisas do Google Health
A publicação do nosso trabalho permite o compartilhamento de ideias e o trabalho colaborativo para a melhoria da saúde. Esta é uma visão completa das nossas publicações e postagens de blog associadas.
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Postagens de blog [mais nos blogs The Keyword e Google Research]
Google’s vision for a healthier futureby Karen DeSalvo
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Postagens de blog
How gen AI can help doctors and nurses ease their administrative workloadsby Aashima Gupta
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Postagens de blog
AI startups revolutionizing mental health careby Karen DeSalvo
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Postagens de blog
Supporting India’s digital health transformationby Bakul Patel
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Postagens de blog
4 principles to guide AI in supporting mental healthby Megan Jones Bell
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Postagens de blog
Google Research at Google I/O 2024by Yossi Matias & James Manyika
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Postagens de blog
How 7 businesses are putting Google Cloud’s AI innovations to workby Carrie Tharp
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Postagens de blog
How we’re using AI to connect people to health informationby Karen DeSalvo
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Postagens de blog
How AI is helping advance women’s health around the worldby Ronit Levavi Morad & Preeti Singh
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Postagens de blog
A new commitment to digital wellbeing for kids and teensby Karen DeSalvo
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Postagens de blog
3 predictions for AI in healthcare in 2024by Aashima Gupta
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Postagens de blog
2023: A year of groundbreaking advances in AI and computingby Jeff Dean, James Manyika, & Demis Hassabis
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Postagens de blog
23 of our biggest moments in 2023by Molly McHugh-Johnson
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Postagens de blog
4 ways we think about health equity and AIby Ivor Horn
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Postagens de blog
5 ways Google is accelerating Health AI innovation in Africaby Yossi Mattia Shravya Shetty
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Postagens de blog
How we’re using AI to help transform healthcareby Yossi Mattias
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Postagens de blog
A new collaboration to improve nutrition informationby Nira Goren
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Postagens de blog
HLTH 2023: Bringing AI to health responsiblyby Michaell Howell
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Postagens de blog
How AI can improve health for everyone, everywhereby Karen DeSalvo
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Postagens de blog
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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Postagens de blog [más en Blog The Keyword de Google & Blog de Google AI]
Looking to the next 75 years of the NHSby Susan Thomas
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Postagens de blog
New research from the UK focused on technology’s role in healthcareby Susan Thomas
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Postagens de blog
Our collaboration with WHO to improve public healthby Karen DeSalvo
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Postagens de blog
Partnering with startups using AI to improve healthcareby Karen DeSalvo
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Postagens de blog
More mental health resources for the moments you need themby Megan Jones Bell
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Postagens de blog
3 ways Google products can help you feel less stressedby Megan Jones Bell
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Postagens de blog
New ways we’re helping people live healthier livesby Karen DeSalvo
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Postagens de blog
Our latest health AI research updatesby Greg Corrado & Yossi Matias
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Postagens de blog
Google Research, 2022 & beyond: Healthby Greg Corrado & Yossi Matias
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Postagens de blog
Meet our Health Equity Research Initiative awardeesby Ivor Horn
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Postagens de blog
7 ways Google is using AI to help solve society's challengesby Katie Malczyk
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Postagens de blog
3 ways to take better care of your mind and body in 2023by Megan Jones Bell
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Postagens de blog
8 things we launched in 2022 to support your healthby Iz Conroy
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Postagens de blog
How to use Google Search to help manage uncertain timesby Hema Budaraju
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Postagens de blog
Unlocking the potential of technology to support healthby Karen DeSalvo
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Postagens de blog
Healthy collaboration: Why partnerships are the heart of healthcare innovationby Aashima Gupta
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Postagens de blog
3 ways AI is scaling helpful technologies worldwideby Jeff Dean
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Postagens de blog
Democratizing access to healthby Karen DeSalvo
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Postagens de blog
Google Assistant offers information and hope for Breast Cancer Awareness Monthby Riva Sciuto
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Postagens de blog
Our work toward health equityby Ivor Horn
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Postagens de blog
Dr. Von Nguyen’s temperature check on public healthby Lauren Winer
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Postagens de blog
Suicide prevention resources on Google Searchby Anne Merritt
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Postagens de blog
Mental health resources you can count onby Megan Jones Bell
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Postagens de blog
Raising awareness of the dangers of fentanylby Megan Jones Bell & Garth Graham
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Postagens de blog
The Check Up: helping people live healthier livesby Karen DeSalvo
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Postagens de blog
The Check Up: our latest health AI developmentsby Greg Corrado
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Postagens de blog
Extending Care Studio with a new healthcare partnershipby Paul Muret
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Postagens de blog
Take a look at Conditions, our new feature in Care Studioby Paul Muret
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Postagens de blog
Google Research: Themes from 2021 and Beyondby Jeff Dean
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Postagens de blog
Making healthcare options more accessible on Searchby Hema Budaraju
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Postagens de blog
HLTH: Building on our commitments in healthby Karen DeSalvo
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Postagens de blog
When it comes to mental health, what are we searching for?by Alicia Cormie
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Postagens de blog
Dr. Ivor Horn talks about technology and health equityby Alicia Cormie
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Postagens de blog
Our Care Studio pilot is expanding to more cliniciansby Paul Muret
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Postagens de blog
Google Research: Looking Back at 2020, and Forward to 2021by Jeff Dean
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Postagens de blog
A new Google Search tool to support women with postpartum depressionby David Feinberg
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Postagens de blog
Prepare for medical visits with help from Google and AHRQby Dave Greenwood
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Postagens de blog
A Collaborative Approach to Shaping Successful UX Critique Practicesby Anna Lurchenko
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Postagens de blog
Learn more about anxiety with a self-assessment on Searchby Daniel Gillison, Jr
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Postagens de blog
Google Research: Looking Back at 2019, and Forward to 2020 and Beyondby Jeff Dean
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Postagens de blog
Lessons Learned from Developing ML for Healthcareby Yun Liu & Po-Hsuan Cameron Chen
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Postagens de blog
Tools to help healthcare providers deliver better careby David Feinberg
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Postagens de blog
Breast cancer and tech...a reason for optimismby Ruth Porat
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Postagens de blog
DeepMind’s health team joins Google Healthby Dominic King
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Postagens de blog
Looking Back at Google’s Research Efforts in 2018by Jeff Dean
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Postagens de blog
Meet David Feinberg, head of Google Healthby Google
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Postagens de blog
AI for Social Good in Asia Pacificby Kent Walter
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Postagens de blog
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)by Jeff Dean
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Postagens de blog
Gain a deeper understanding of Posttraumatic Stress Disorder on Googleby Paula Schnurr & Teri Brister
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Postagens de blog
Learning more about clinical depression with the PHQ-9 questionnaireby Mary Giliberti
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Postagens de blog
Partnering on machine learning in healthcareby Katherine Chou
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Postagens de blog
The Google Brain Team — Looking Back on 2016by Jeff Dean
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Posts de blog sobre Covid-19
Supporting evolving COVID information needsby Hema Budaraju
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Posts de blog sobre Covid-19 [más en Blog The Keyword de Google]
Group effort: How we helped launch an NYC vaccine siteby Lauren Gallagher
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Posts de blog sobre Covid-19 [más en Blog The Keyword de Google]
This year, we searched for ways to stay healthyby Hema Budaraju
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Posts de blog sobre Covid-19
New tools to support vaccine access and distributionby Tomer Shekel
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Posts de blog sobre Covid-19
An update on our COVID response prioritiesby the COVID Response team, Google India
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Posts de blog sobre Covid-19
Our commitment to COVID-19 vaccine equityby Karen DeSalvo
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Posts de blog sobre Covid-19
How anonymized data helps fight against diseaseby Stephen Ratcliffe
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Posts de blog sobre Covid-19
How we’re helping get vaccines to more peopleby Sundar Pichai
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Posts de blog sobre Covid-19
Exposure Notifications: end of year updateby Steph Hannon
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Posts de blog sobre Covid-19
How you'll find accurate and timely information on COVID-19 vaccinesby Karen DeSalvo & Kristie Canegallo
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Posts de blog sobre Covid-19
How I’m giving thanks (and staying safe) this Thanksgivingby Karen DeSalvo
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Posts de blog sobre Covid-19
A Q&A on coronavirus vaccinesGoogle Keyword Blog
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Posts de blog sobre Covid-19
An update on our efforts to help Americans navigate COVID-19by Ruth Porat
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Posts de blog sobre Covid-19
Making data useful for public healthby Katherine Chou
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Posts de blog sobre Covid-19
Google supports COVID-19 AI and data analytics projectsby Mollie Javerbaum & Meghan Houghton
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Posts de blog sobre Covid-19
Using symptoms search trends to inform COVID-19 researchby Evgeniy Gabrilovich
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Posts de blog sobre Covid-19
An update on Exposure Notificationsby Dave Burke
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Posts de blog sobre Covid-19
Exposure Notification API launches to support public health agenciesby Apple & Google
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Posts de blog sobre Covid-19
Dr. Karen DeSalvo on ‘putting information first’ during COVID-19by Megan Washam
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Posts de blog sobre Covid-19
Resources for mental health support during COVID-19by David Feinberg
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Posts de blog sobre Covid-19
Helping you avoid COVID-19 online security risksGoogle Africa Blog
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Posts de blog sobre Covid-19
Apple and Google partner on COVID-19 contact tracing technologyby Apple & Google
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Posts de blog sobre Covid-19
Connecting people to virtual care optionsby Julie Black
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Posts de blog sobre Covid-19
Support for public health workers fighting COVID-19by Karen DeSalvo
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Posts de blog sobre Covid-19
Helping public health officials combat COVID-19by Jen Fitzpatrick & Karen DeSalvo
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Posts de blog sobre Covid-19
Connecting people with COVID-19 information and resourcesby Emily Moxley
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Posts de blog sobre Covid-19
COVID-19: How we’re continuing to helpby Sundar Pichai
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Posts de blog sobre Covid-19
Coronavirus: How we’re helpingby Sundar Pichai
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Avaliações
Safety principles for medical summarization using generative AIObika, D., Kelly, C., Ding, N., Farrance, C., Krause, J., Mittal, P., Cheung, D., Cole-Lewis, H., Elish, M., Karthikesalingam, A., Webster, D., Patel, B. & Howell, M.
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Avaliações
A multiparty collaboration to engage diverse populations in community-centered artificial intelligence researchDevon-Sand, A., Sayres, R., Liu, Y., Strachan, P., Smith, M. A., Nguyen, T., Ko, J. M. & Lin, S.
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Avaliações
The Opportunities and Risks of Large Language Models in Mental HealthLawrence, H. R., Schneider, R. A., Rubin, S. B.,Matarić, M. J., McDuff, D. J. & Jones Bell, M.
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The Regulation of Clinical Artificial IntelligenceBlumenthal David & Patel Bakul.
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Generative artificial intelligence, patient safety and healthcare quality: a reviewHowell, M. D.
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Avaliações
AI in Action: Accelerating Progress Towards the Sustainable Development GoalsGosselink, B. H., Brandt, K., Croak, M., DeSalvo, K., Gomes, B., Ibrahim, L., Johnson, M., Matias, Y., Porat, R., Walker, K. & Manyika, J.
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Transforming Public Health Practice With Generative Artificial IntelligenceBharel, M., Auerbach, J., Nguyen, V. & DeSalvo, K. B.
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Information is a determinant of healthGraham, G., Goren, N., Sounderajah, V. & DeSalvo, K.
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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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Three Epochs of Artificial Intelligence in Health CareHowell M., Corrado G., DeSalvo K.
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Artificial intelligence in healthcare: a perspective from GoogleLehmann, L. S., Natarajan, V. & Peng, L. Chapter 39
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Explaining counterfactual imagesLang, O., Traynis, I. & Liu, Y.
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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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Deep Learning for Epidemiologists: An introduction to neural networks.Serghiou, S. & Rough, K.
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Building a Clinical Team in a Large Technology Company.DeSalvo Karen B. & Howell Michael D.
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Medicine’s Role in Reimagining Public Health: Reuniting Panacea and HygeiaDeSalvo, K. B., Kadakia, K. T. & Chokshi, D. A.
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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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Public Health 3.0 After COVID-19-Reboot or Upgrade?DeSalvo, K. B. & Kadakia, K. T.
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Avaliações
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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Evaluation of artificial intelligence on a reference standard based on subjective interpretationChen, P.-H. C., Mermel, C. H. & Liu, Y.
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Artificial Intelligence in MedicineKelly, C. J., Brown, A. P. Y. & Taylor, J. A.
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Challenges of Accuracy in Germline Clinical Sequencing DataPoplin, R., Zook, J. M. & DePristo, M.
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Retinal detection of kidney disease and diabetesMitani, A., Hammel, N. & Liu, Y.
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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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Closing the translation gap: AI applications in digital pathologySteiner, D. F., Chen, P.-H. C. & Mermel, C. H.
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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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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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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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Artificial intelligence, machine learning and deep learning for eye care specialistsSayres, R., Hammel, N. & Liu, Y.
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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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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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Key challenges for delivering clinical impact with artificial intelligenceKelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.
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Ensuring Fairness in Machine Learning to Advance Health EquityRajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H.
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Avaliações
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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How to develop machine learning models for healthcareChen, C. P.-H., Liu, Y., & Peng, L.
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Machine Learning in MedicineRajkomar, A., Dean, J., & Kohane I.
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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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When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ dataRough K, Thompson J.
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Resolving the Productivity Paradox of Health Information Technology: A Time for OptimismWachter, R. M., Howell, M. D.
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Postagens de blog
Developing reliable AI tools for healthcareby Krishnamurthy (Dj) Dvijotham & Taylan Cemgil
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Postagens de blog
Robust and efficient medical imaging with self-supervisionby Shekoofeh Azizi & Laura Culp
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Postagens de blog
How Underspecification Presents Challenges for Machine Learningby Alex D’Amour & Katherine Heller
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Postagens de blog
Self-Supervised Learning Advances Medical Image Classificationby Shekoofeh Azizi
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Publicações
Generative models improve fairness of medical classifiers under distribution shifts.Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A. & Gowal, S.
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Publicações
Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.Martindale, A. P. L., Ng, B., Ngai, V., Kale, A. U., Ferrante di Ruffano, L., Golub, R. M., Collins, G. S., Moher, D., McCradden, M. D., Oakden-Rayner, L., Rivera, S. C., Calvert, M., Kelly, C. J., Lee, C. S., Yau, C., Chan, A.-W., Keane, P. A., Beam, A. L., Denniston, A. K. & Liu, X.
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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Postagens de blog
How we’re using AI to connect people to health information -
Postagens de blog
3 ways we are building equity into our health workby Ivor Horn
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Postagens de blog
SCIN: A new resource for representative dermatology imagesby Pooja Rao
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Postagens de blog
HEAL: A framework for health equity assessment of machine learning performanceby Mike Schaekermann & Ivor Horn
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Postagens de blog
Health-specific embedding tools for dermatology and pathologyby Dave Steiner & Rory Pilgrim
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Postagens de blog
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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Postagens de blog
8 ways Google Lens can help make your life easierby Lou Wang
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Postagens de blog
Ask a Techspert: What does AI do when it doesn’t know?by Iz Conroy
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Postagens de blog
Does Your Medical Image Classifier Know What It Doesn’t Know?by Abhijit Guha Roy & Jie Ren
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Postagens de blog
How DermAssist uses TensorFlow.js for on-device image quality checksby Miles Hutson & Aaron Loh
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Postagens de blog
Using AI to help find answers to common skin conditionsby Peggy Bui & Yuan Liu
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Postagens de blog
AI assists doctors in interpreting skin conditionsby Ayush Jain & Peggy Bui
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Postagens de blog
Generating Diverse Synthetic Medical Image Data for Training Machine Learning Modelsby Timo Kohlberger & Yuan Liu
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Postagens de blog
Using Deep Learning to Inform Differential Diagnoses of Skin Diseasesby Yuan Liu & Peggy Bui
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Publicações
Searching for dermatology information online using images vs text: A randomized studyKrogue, J. D., Sayres, R., Hartford, J., Talreja, A., Bavishi, P., Salaets, N., Raiford, K., Nayar, J., Patel, R., Matias, Y., Corrado, G. S., Berrada, D., Kharbanda, H., Wang, L., Webster, D. R., Duong, Q., Bui, P. & Liu, Y.
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Publicações
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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Postagens de blog
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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Conformal prediction under ambiguous ground truthStutz, D., Roy, A. G., Matejovicova, T., Strachan, P., Cemgil, A. T. & Doucet, A.
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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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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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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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Publicações
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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Publicações
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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Publicações
Addressing the Real-world Class Imbalance Problem in DermatologyWeng, W.-H., Deaton, J., Natarajan, V., Elsayed, G. F. & Liu, Y.
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Publicações
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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Publicações
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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Publicações
DermGAN: Synthetic Generation of Clinical Skin Images with PathologyGhorbani, A., Natarajan, V., Coz, D. & Liu, Y.
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Publicações
Measuring clinician-machine agreement in differential diagnoses for dermatologyEng, C., Liu, Y. & Bhatnagar, R.
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Postagens de blog
Improved Detection of Elusive Polyps via Machine Learningby Yossi Matias & Ehud Rivlin
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Postagens de blog
Verily Opens New R&D Center in Israel Focused on the Application of AI in Healthcareby Robin Suchan
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Postagens de blog
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screeningsby Daniel Freedman & Ehud Rivlin
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Publicações
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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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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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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An ML-Based Framework for COVID-19 Epidemiologyby Joel Shor & Sercan Arik
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Postagens de blog
Google Cloud, Harvard Global Health Institute release improved COVID-19 Public Forecasts, share lessons learnedby Tomas Pfister
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Postagens de blog
Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID-19 forecasting modelby Dario Sava
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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
Interpretable Sequence Learning for Covid-19 ForecastingArik, Li, Yoon, Sinha, Epshteyn, Le, Menon, Singh, Zhang, Nikoltchev, Sonthalia, Nakhost, Kanal & Pfister.
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Postagens de blog
How AI is making eyesight-saving care more accessible in resource-constrained settingsby Rajroshan Sawhney
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Postagens de blog
Supporting a healthier and greener India with our AIby the Google India Team
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Postagens de blog
Google at 25: By the numbersby Michelle Budzyna & Molly McHugh-Johnson
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Postagens de blog
7 ways Google Health is improving outcomes in Asia Pacificby Karen DeSalvo
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Postagens de blog
5 myths about medical AI, debunkedby Kasumi Widner
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Postagens de blog
An eye to the future: How AI could help to improve detection of eye disease in Australian communitiesby Angus Turner
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Postagens de blog
Healthcare AI systems that put people at the centerby Emma Beede
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Postagens de blog
The Check Up: our latest health AI developmentsby Greg Corrado
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Postagens de blog
New milestones in helping prevent eye disease with Verilyby Kasumi Widner & Sunny Virmani
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Postagens de blog
Launching a powerful new screening tool for diabetic eye disease in India -
Postagens de blog
AI for Social Good in Asia Pacificby Kent Walter
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Postagens de blog
Improving the Effectiveness of Diabetic Retinopathy Modelsby Rory Sayres & Jonathan Krause
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Postagens de blog
A major milestone for the treatment of eye diseaseby Mustafa Suleyman
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Postagens de blog
Detecting diabetic eye disease with machine learningby Lily Peng
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Postagens de blog
Deep learning for Detection of Diabetic Eye Diseaseby Lily Peng & Varun Gulshan
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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image AssessmentSchaekermann, M., Cai, C. J., Huang, A. E. & Sayres, R.
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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
Who Said What: Modeling Individual Labelers Improves ClassificationGuan, M., Gulshan, V., Dai, A, Hinton, G.
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Publicações
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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Postagens de blog [mais no blog The Keyword do Google]
Loss of Pulse Detection: A first-of-its-kind feature on Pixel Watch 3by Tajinder Gadh & Pramod Rudrapatna
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A new study using Fitbit data uncovers connections between sleep and diseaseby Logan Schneider & Evan Brittain
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Health partners can now more easily access Fitbit heart databy Kapil Parakh
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How Fitbit can help you measure stress — and use it to your advantageby Molly McHugh-Johnson
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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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6 things I learned after using the Fitbit Charge 6 for a weekby Mike Darling
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New Fitbit study explores metabolic healthby Javier L. Prieto
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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 healthGoogle Keyword Blog | 21-Dec-2022
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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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The Check Up: helping people live healthier livesby Karen DeSalvo
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Publicações
Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research ProgramZheng, N. S., Annis, J., Master, H., Han, L., Gleichauf, K., Ching, J. H., Nasser, M., Coleman, P., Desine, S., Ruderfer, D. M., Hernandez, J., Schneider, L. D. & Brittain, E. L.
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Publicações
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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Publicações
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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Publicações
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19Natarajan, A., Su, H.-W. & Heneghan, C.
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Publicações
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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Postagens de blog [mais informações no Blog DeepVariant]
Learning DeepVariant's hidden powersby Atilla Kiraly & Yuchen Zhou
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A breakthrough to better represent human genetic diversityby Andrew Carroll
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Building better pangenomes to improve the equity of genomicsby Andrew Carroll & Kishwar Shafin
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Personalized pangenome referencesSirén, J., Eskandar, P., Ungaro, M. T., Hickey, G., Eizenga, J. M., Novak, A. M., Chang, X., Chang, P.-C., Kolmogorov, M., Carroll, A., Monlong, J. & Paten, B.
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Local read haplotagging enables accurate long-read small variant callingKolesnikov, A., Cook, D., Nattestad, M. et al.
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Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and predictionYun, T., Cosentino, J., Behsaz, B., McCaw, Z. R., Hill, D., Luben, R., Lai, D., Bates, J., Yang, H., Schwantes-An, T.-H., Zhou, Y., Khawaja, A. P., Carroll, A., Hobbs, B. D., Cho, M. H., McLean, C. Y. & Hormozdiari, F.
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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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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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Publicações
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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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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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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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Publicações
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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Publicações
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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Publicações
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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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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Publicações
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic RegressionYadlowsky, S., Yun, T., McLean, C. & D’Amour, A.
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Publicações
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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Publicações
GenomeWarp: an alignment-based variant coordinate transformationMcLean, C. Y., Hwang, Y., Poplin, R. & DePristo, M. A.
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Publicações
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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Publicações
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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Publicações
Deep learning of genomic variation and regulatory network dataTelenti, A., Lippert, C., Chang, P.-C. & DePristo, M.
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Publicações
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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Soli-enabled noncontact heart rate detection for sleep and meditation trackingXu, L., Lien, J., Li, H., Gillian, N., Nongpiur, R., Li, J., Zhang, Q., Cui, J., Jorgensen, D., Bernstein, A., Bedal, L., Hayashi, E., Yamanaka, J., Lee, A., Wang, J., Shin, D., Poupyrev, I., Thormundsson, T., Pathak, A. & Patel, S.
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Audioplethysmography for Cardiac Monitoring in HearablesFan, X., Pearl, D., Howard, R., Shangguan, L. & Thormundsson, T. APG.
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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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Digital biomarker of mental fatigueTseng, V. W.-S., Valliappan, N., Ramachandran, V., Choudhury, T. & Navalpakkam, V.
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Accelerating eye movement research via accurate and affordable smartphone eye tracking.Valliappan, N., Dai, N., Steinberg, E., He, J., Rogers, K., Ramachandran, V., Xu, P., Shojaeizadeh, M., Guo, L., Kohlhoff, K. & Navalpakkam, V.
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Scaling wearable foundation modelsNarayanswamy, G., Liu, X., Ayush, K., Yang, Y., Xu, X., Liao, S., Garrison, J., Tailor, S., Sunshine, J., Liu, Y., Althoff, T., Narayanan, S., Kohli, P., Zhan, J., Malhotra, M., Patel, S., Abdel-Ghaffar, S. & McDuff, D.
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Towards Democratization of Subspeciality Medical ExpertiseO'Sullivan, J.W., Palepu, A., Saab, K., Weng, W.H., Cheng, Y., Chu, E., Desai, Y., Elezaby, A., Kim, D.S., Lan, R. and Tang, W., 2024.
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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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PathAlign: A vision-language model for whole slide images in histopathologyAhmed, F., Sellergren, A., Yang, L., Xu, S., Babenko, B., Ward, A., Olson, N., Mohtashamian, A., Matias, Y., Corrado, G. S., Duong, Q., Webster, D. R., Shetty, S., Golden, D., Liu, Y., Steiner, D. F. & Wulczyn, E.
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Towards a Personal Health Large Language ModelCosentino, J., Belyaeva, A., Liu, X., Furlotte, N. A., Yang, Z., Lee, C., Schenck, E., Patel, Y., Cui, J., Schneider, L. D., Bryant, R., Gomes, R. G., Jiang, A., Lee, R., Liu, Y., Perez, J., Rogers, J. K., Speed, C., Tailor, S., Walker, M., Yu, J., Althoff, T., Heneghan, C., Hernandez, J., Malhotra, M., Stern, L., Matias, Y., Corrado, G. S., Patel, S., Shetty, S., Zhan, J., Prabhakara, S., McDuff, D. & McLean, C. Y.
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Transforming Wearable Data into Health Insights using Large Language Model AgentsMerrill, M. A., Paruchuri, A., Rezaei, N., Kovacs, G., Perez, J., Liu, Y., Schenck, E., Hammerquist, N., Sunshine, J., Tailor, S., Ayush, K., Su, H.-W., He, Q., McLean, C. Y., Malhotra, M., Patel, S., Zhan, J., Althoff, T., McDuff, D. & Liu, X.
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Tx-LLM: A Large Language Model for TherapeuticsChaves, J. M. Z., Wang, E., Tu, T., Vaishnav, E. D., Lee, B., Sara Mahdavi, S., Semturs, C., Fleet, D., Natarajan, V. & Azizi, S.
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Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM.Li, B., Wang, A., Strachan, P., Séguin, J. A., Lachgar, S., Schroeder, K. C., Fleck, M. S., Wong, R., Karthikesalingam, A., Natarajan, V., Matias, Y., Corrado, G. S., Webster, D., Liu, Y., Hammel, N., Sayres, R., Semturs, C. & Schaekermann, M.
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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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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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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 TransductionEl Shafey, L., Soltau, H. & Shafran, I.
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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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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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Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep LearningWeng, W.-H., Baur, S., Daswani, M., Chen, C., Harrell, L., Kakarmath, S., Jabara, M., Behsaz, B., McLean, C. Y., Matias, Y., Corrado, G. S., Shetty, S., Prabhakara, S., Liu, Y., Danaei, G. & Ardila, D.
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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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Estrogen receptor gene expression prediction from H&E whole slide imagesSrinivas, A. A., Jaroensri, R., Wulczyn, E., Liu, Y., Wren, J. H., Thompson, E. E., Olson, N., Beckers, F., Miao, M., Chen, P.-H. C. & Steiner, D. F.
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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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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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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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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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How we’re using AI to combat floods, wildfires and extreme heatby Yossi Matias
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How we’re helping people and cities adapt to extreme heatby Kate Brandt
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An update on our efforts to help Americans navigate COVID-19by Ruth Porat
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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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Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location dataYoung, M. T., Vispute, S., Serghiou, S., Kumok, A., Shah, Y., Lane, K. J., Black-Ingersoll, F., Brochu, P., Bharel, M., Skenazy, S., Karthikesalingam, A., Bavadekar, S., Kansal, M., Shekel, T., Gabrilovich, E. & Wellenius, G. A.
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Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis.Banke-Thomas, A., Wong, K. L. M., Olubodun, T., Macharia, P. M., Sundararajan, N., Shah, Y., Prasad, G., Kansal, M., Vispute, S., Shekel, T., Ogunyemi, O., Gwacham-Anisiobi, U., Wang, J., Abejirinde, I.-O. O., Makanga, P. T., Azodoh, N., Nzelu, C., Afolabi, B. B., Stanton, C. & Beňová, L.
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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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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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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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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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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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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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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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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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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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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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Prospective multi-site validation of AI to detect tuberculosis and chest X-ray abnormalitiesKazemzadeh, S., Kiraly, A. P., Nabulsi, Z., Sanjase, N., Maimbolwa, M., Shuma, B., Jamshy, S., Chen, C., Agharwal, A., T. Lau, C., Sellergren, A., Golden, D., Yu, J., Wu, E., Matias, Y., Chou, K., Corrado, G. S., Shetty, S., Tse, D., Eswaran, K., Liu, Y., Pilgrim, R., Muyoyeta, M. & Prabhakara, S.
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Artificial intelligence as a second reader for screening mammography.Nakai, E., Miyagi, Y., Suzuki, K., Scoccia Pappagallo, A., Kayama, H., Matsuba, T., Yang, L., Xu, S., Kelly, C., Najafi, R., Kohlberger, T., Golden, D., Uddin, A., Nakamura, Y., Kokubu, Y., Takahashi, Y., Ueno, T., Oguchi, M., Ohno, S. & Ledsam, J. R.
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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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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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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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
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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Publicações
Improving reference standards for validation of AI-based radiographyDuggan, G. E., Reicher, J. J., Liu, Y., Tse, D. & Shetty, S.
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Publicações
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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Publicações
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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Publicações
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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Postagens de blog [mais informações no Blog oficial do YouTube]
Exploring how AI tools can help increase high-quality health contentby Garth Graham & Viknesh Sounderajah
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Postagens de blog
How we’re using AI to connect people to health informationGoogle Keyword Blog | 19-Mar-2024
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Postagens de blog
Safer Internet Day: Supporting teen mental health and wellbeing on YouTubeby The YouTube Team
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Postagens de blog
Elevating first aid information on YouTube searchby Garth Graham
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Postagens de blog
How AI helps make public health truly publicby Garth Graham
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Postagens de blog
Continued support for teen wellbeing and mental health on YouTubeby James Beser
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Postagens de blog
Expanding equitable access to health information on YouTubeby Garth Graham
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Postagens de blog [más en Blog oficial de YouTube]
A long term vision for YouTube’s medical misinformation policiesYoutube Official Blog
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Postagens de blog
New ways for UK licensed healthcare professionals to reach viewers on YouTubeYoutube Official Blog
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Postagens de blog
Mental Health Action Day: Small steps to support your mental healthYoutube Official Blog
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Postagens de blog
An updated approach to eating disorder-related contentby Garth Graham
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Postagens de blog
Finding connection and support this World Mental Health Dayby Jessica DiVento Dzuban
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Postagens de blog
Expanding clinicians’ access to Continuing Educationby Garth Graham
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Postagens de blog
8 things we launched in 2022 to support your healthby Iz Conroy
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Postagens de blog
New ways for licensed healthcare professionals to reach people on YouTubeby Garth Graham
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Postagens de blog
Answering the human questions: How we’re putting patient voices front and centerby Garth Graham
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Postagens de blog
Our work toward health equityby Ivor Horn
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Postagens de blog
Introducing THE-IQ: tackling health equity with YouTube Health and Kaiser Family Foundationby Garth Graham
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Postagens de blog
New ways to answer your health questions in the United Kingdomby Garth Graham
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Postagens de blog
The Check Up: helping people live healthier livesby Karen DeSalvo
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Postagens de blog
Answering your health questions in Brazil, India, and Japanby Garth Graham
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Postagens de blog
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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Postagens de blog
Doctors bring their expertise on vaccines to YouTubeby Garth Graham
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Postagens de blog
Introducing new ways to help you find answers to your health questionsby Garth Graham