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Google for Health - Health Research Publications

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Health research publications

Publishing our work allows us to share ideas and work collaboratively to advance healthcare. This is a comprehensive view of our publications and associated blog posts.

General

Blog Posts [more at Google Keyword Blog & Google Research Blog]

COVID-19 blog posts [more at Google Keyword Blog]

Reviews

AI, health, and health care today and tomorrow: The JAMA Summit report on artificial intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., Chen, F., Cohen, I. G., Denniston, A., Desai, S., Embí, P., Faisal, A., Ferryman, K., Gerhart, J., Gross, M., Hernandez-Boussard, T., Howell, M., Johnson, K., Lee, K., Liu, X., Lomis, K., London, A. J., Longhurst, C. A., Mandl, K., McGlynn, E., Mello, M. M., Munoz, F., Ohno-Machado, L., Ouyang, D., Perlis, R., Phillips, A., Rhew, D., Ross, J. S., Saria, S., Schwamm, L., Seymour, C. W., Shah, N. H., Shah, R., Singh, K., Solomon, M., Spates, K., Spector-Bagdady, K., Wang, T., Gichoya, J. W., Weinstein, J., Wiens, J., Bibbins-Domingo, K.
JAMA (2025).
The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., Bossuyt, P. M., Darzi, A., Ashrafian, H. & STARD-AI Steering Committee.
Nat. Med. 1–7 (2025).
Prompting introspection

Shreibati, J. B.
Circulation 151, 1375–1377 (2025)
AI's Capabilities Make It a Powerful Tool for Driving Societal Impact

Matias, Y., Hassidim, A., Nelson, P.
The National Academies Press. (2025)
Oculomics: Current concepts and evidence

Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., Masalkhi, M., Suh, A., Tham, Y. C., Cheung, C. Y., Yang, X., Yu, H., Ge, Z., Wang, W., Sheng, B., Liu, Y., Lee, A. G., Denniston, A. K., van Wijngaarden, P., Keane, P. A., Cheng, C.-Y., He, M. & Wong, T. Y.
Prog. Retin. Eye Res. 106, 101350 (2025)
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Alderman, J. E., Palmer, J., Laws, E., McCradden, M. D., Ordish, J., Ghassemi, M., Pfohl, S. R., Rostamzadeh, N., Cole-Lewis, H., Glocker, B., Calvert, M., Pollard, T. J., Gill, J., Gath, J., Adebajo, A., Beng, J., Leung, C. H., Kuku, S., Farmer, L.-A., Matin, R. N., Mateen, B. A., McKay, F., Heller, K., Karthikesalingam, A., Treanor, D., Mackintosh, M., Oakden-Rayner, L., Pearson, R., Manrai, A. K., Myles, P., Kumuthini, J., Kapacee, Z., Sebire, N. J., Nazer, L. H., Seah, J., Akbari, A., Berman, L., Gichoya, J. W., Righetto, L., Samuel, D., Wasswa, W., Charalambides, M., Arora, A., Pujari, S., Summers, C., Sapey, E., Wilkinson, S., Thakker, V., Denniston, A. & Liu, X.
Lancet Digit. Health 7, e64–e88 (2025).
Inequalities in geographical access to emergency obstetric and newborn care

Banke-Thomas, A., Beňová, L., Ray, N., Wong, K. L., Stanton, C., Shetty, S. & Afolabi, B. B.
Bull. World Health Organ. 102, 837–839 (2024).
Safety principles for medical summarization using generative AI

Obika, D., Kelly, C., Ding, N., Farrance, C., Krause, J., Mittal, P., Cheung, D., Cole-Lewis, H., Elish, M., Karthikesalingam, A., Webster, D., Patel, B. & Howell, M.
Nat. Med. 1–3 (2024). [readcube]

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A multiparty collaboration to engage diverse populations in community-centered artificial intelligence research

Devon-Sand, A., Sayres, R., Liu, Y., Strachan, P., Smith, M. A., Nguyen, T., Ko, J. M. & Lin, S.
Mayo Clinic Proceedings: Digital Health 2, 463–469 (2024).
The Opportunities and Risks of Large Language Models in Mental Health

Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J. & Jones Bell, M.
JMIR Ment Health 11, e59479 (2024).
The Regulation of Clinical Artificial Intelligence

Blumenthal David & Patel Bakul.
NEJM AI 1, AIpc2400545 (2024).
Generative artificial intelligence, patient safety and healthcare quality: a review

Howell, M. D.
BMJ Qual. Saf. (2024).
AI in Action: Accelerating Progress Towards the Sustainable Development Goals

Gosselink, B. H., Brandt, K., Croak, M., DeSalvo, K., Gomes, B., Ibrahim, L., Johnson, M., Matias, Y., Porat, R., Walker, K. & Manyika, J.
arXiv [cs.CY] (2024).
Transforming Public Health Practice With Generative Artificial Intelligence

Bharel, M., Auerbach, J., Nguyen, V. & DeSalvo, K. B.
Health Aff. 43, 776–782 (2024).
Information is a determinant of health

Graham, G., Goren, N., Sounderajah, V. & DeSalvo, K.
Nat. Med. (2024). [readcube]

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An intentional approach to managing bias in general purpose embedding models

Weng, W.-H., Sellergen, A., Kiraly, A. P., D’Amour, A., Park, J., Pilgrim, R., Pfohl, S., Lau, C., Natarajan, V., Azizi, S., Karthikesalingam, A., Cole-Lewis, H., Matias, Y., Corrado, G. S., Webster, D. R., Shetty, S., Prabhakara, S., Eswaran, K., Celi, L. A. G. & Liu, Y.
The Lancet Digital Health 6, e126–e130 (2024).
Three Epochs of Artificial Intelligence in Health Care

Howell M., Corrado G., DeSalvo K.
JAMA. 331(3):242–244 (2024).
Artificial intelligence in healthcare: a perspective from Google

Lehmann, L. S., Natarajan, V. & Peng, L. Chapter 39
(ed. Krittanawong, C.) Artificial Intelligence in Clinical Practice. 341–344 (Academic Press, 2024).
Explaining counterfactual images

Lang, O., Traynis, I. & Liu, Y.
Nat Biomed Eng (2023).[readcube]

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Beyond Predictions: Explainability and Learning from Machine Learning

Deng, C.-Y., Mitani, A., Chen, C. W., Peng, L. H., Hammel, N. & Liu, Y.
(eds. Yogesan, K., Goldschmidt, L., Cuadros, J. & Ricur, G.) 199–218. Springer International Publishing, 2023. [readcube]

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Deep Learning for Epidemiologists: An introduction to neural networks

Serghiou, S. & Rough, K.
Am. J. Epidemiol. (2023).
Building a Clinical Team in a Large Technology Company

DeSalvo Karen B. & Howell Michael D.
NEJM Catalyst non-issue commentary (2023).
Medicine’s Role in Reimagining Public Health: Reuniting Panacea and Hygeia

DeSalvo, K. B., Kadakia, K. T. & Chokshi, D. A.
JAMA Health Forum 2, e214051–e214051 (2021).
Modernizing Public Health Data Systems: Lessons From the Health Information Technology for Economic and Clinical Health (HITECH) Act

Kadakia, K. T., Howell, M. D. & DeSalvo, K. B.
JAMA 326, 385–386 (2021).
Public Health 3.0 After COVID-19-Reboot or Upgrade?

DeSalvo, K. B. & Kadakia, K. T.
Am. J. Public Health 111, S179–S181 (2021).
A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

Sounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., Kahn, C. E., Jr, Esteva, A., Karthikesalingam, A., Mateen, B., Webster, D., Milea, D., Ting, D., Treanor, D., Cushnan, D., King, D., McPherson, D., Glocker, B., Greaves, F., Harling, L., Ordish, J., Cohen, J. F., Deeks, J., Leeflang, M., Diamond, M., McInnes, M. D. F., McCradden, M., Abràmoff, M. D., Normahani, P., Markar, S. R., Chang, S., Liu, X., Mallett, S., Shetty, S., Denniston, A., Collins, G. S., Moher, D., Whiting, P., Bossuyt, P. M. & Darzi, A.
Nat. Med. (2021).
Evaluation of artificial intelligence on a reference standard based on subjective interpretation

Chen, P.-H. C., Mermel, C. H. & Liu, Y.
The Lancet Digital Health (2021). doi:10.1016/S2589-7500(21)00216-8
Artificial Intelligence in Medicine

Kelly, C. J., Brown, A. P. Y. & Taylor, J. A.
(eds. Lidströmer, N. & Ashrafian, H.) 1–18 (Springer International Publishing, 2021).
Challenges of Accuracy in Germline Clinical Sequencing Data

Poplin, R., Zook, J. M. & DePristo, M.
JAMA 326, 268–269 (2021).
Retinal detection of kidney disease and diabetes

Mitani, A., Hammel, N. & Liu, Y.
Nature Biomedical Engineering 1–3 (2021). [readcube]

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Deep learning-enabled medical computer vision

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J. & Socher, R.
npj Digital Medicine 4, 5 (2021).
Closing the translation gap: AI applications in digital pathology

Steiner, D. F., Chen, P.-H. C. & Mermel, C. H.
Biochim. Biophys. Acta Rev. Cancer 1875, 188452 (2021).
Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs

Liu, Y., Yang, L., Phene, S. & Peng, L.
Artificial Intelligence in Medicine 247–264 (2021).
Resonate: Reaching Excellence Through Equity, Diversity, and Inclusion in ISMRM

Warnert, E. A. H., Kasper, L., Meltzer, C. C., Lightfoote, J. B., Bucknor, M. D., Haroon, H., Duggan, G., Gowland, P., Wald, L., Miller, K. L., Morris, E. A. & Anazodo, U. C.
J. Magn. Reson. Imaging (2020). doi:10.1002/jmri.27476 [readcube]

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Current and future applications of artificial intelligence in pathology: a clinical perspective

Rakha, E. A., Toss, M., Shiino, S., Gamble, P., Jaroensri, R., Mermel, C. H. & Chen, P.-H. C.
J. Clin. Pathol. (2020). doi:10.1136/jclinpath-2020-206908
Artificial intelligence, machine learning and deep learning for eye care specialists

Sayres, R., Hammel, N. & Liu, Y.
Annals of Eye Science 5, 18–18 (2020).
Artificial intelligence in digital breast pathology: Techniques and applications

Ibrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C. & Rakha, E. A.
Breast 49, 267–273 (2020).
How to Read Articles That Use Machine Learning: Users’ Guides to the Medical Literature

Liu, Y., Chen, P.-H. C., Krause, J. & Peng, L.
JAMA 322, 1806–1816 (2019). [readcube]

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Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D.
BMC Med. 17, 195 (2019).
Artificial Intelligence Approach in Melanoma

Curiel-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.
(eds. Fisher, D. E. & Bastian, B. C.) 599–628. Springer New York ( 2019).
How to develop machine learning models for healthcare

Chen, C. P.-H., Liu, Y., & Peng, L.
Nat. Mater. 18, 410–414 (2019). [readcube]

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Machine Learning in Medicine

Rajkomar, A., Dean, J., & Kohane I.
N. Engl. J. Med. 380:1347-1358 (2019).
A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S. & Dean, J.
Nat. Med. 25, 24–29 (2019). [readcube]

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Ensuring Fairness in Machine Learning to Advance Health Equity

Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H.
Ann. Intern. Med. 169(12):866-872 (2018).
When does size matter? -- Promises, pitfalls, and appropriate interpretations of ‘big’ data

Rough K, Thompson J.
Ophthalmology. 125(8):1136-1138 (2018).
Resolving the Productivity Paradox of Health Information Technology: A Time for Optimism

Wachter, R. M., Howell, M. D.
JAMA 320(1):25-26 (2018).

Biomedical Large Language Models

Blog Posts [more at Med-PaLM site]

Publications

Cross-Specialty Applied AI

Blog Posts

Publications

Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness

Pfohl, S. R., Harris, N., Nagpal, C., Madras, D., Mhasawade, V., Salaudeen, O., Dieng, A., Sequeira, S., Arciniegas, S., Sung, L., Ezeanochie, N., Cole-Lewis, H., Heller, K., Koyejo, S. & D’Amour, A.
arXiv [stat.ML] (2025).
MedGemma Technical Report

Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., Chen, J., Mahvar, F., Yatziv, L., Chen, T., Sterling, B., Baby, S. A., Baby, S. M., Lai, J., Schmidgall, S., Yang, L., Chen, K., Bjornsson, P., Reddy, S., Brush, R., Philbrick, K., Hu, H., Yang, H., Tiwari, R., Jansen, S., Singh, P., Liu, Y., Azizi, S., Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Riviere, M., Rouillard, L., Mesnard, T., Cideron, G., Grill, J.-B., Ramos, S., Yvinec, E., Casbon, M., Buchatskaya, E., Alayrac, J.-B., Lepikhin, D., Feinberg, V., Borgeaud, S., Andreev, A., Hardin, C., Dadashi, R., Hussenot, L., Joulin, A., Bachem, O., Matias, Y., Chou, K., Hassidim, A., Goel, K., Farabet, C., Barral, J., Warkentin, T., Shlens, J., Fleet, D., Cotruta, V., Sanseviero, O., Martins, G., Kirk, P., Rao, A., Shetty, S., Steiner, D. F., Kirmizibayrak, C., Pilgrim, R., Golden, D. & Yang, L.
arXiv [cs.AI] (2025).
Health AI Developer Foundations

Kiraly, A. P., Baur, S., Philbrick, K., Mahvar, F., Yatziv, L., Chen, T., Sterling, B., George, N., Jamil, F., Tang, J., Bailey, K., Ahmed, F., Goel, A., Ward, A., Yang, L., Sellergren, A., Matias, Y., Hassidim, A., Shetty, S., Golden, D., Azizi, S., Steiner, D. F., Liu, Y., Thelin, T., Pilgrim, R. & Kirmizibayrak, C.
arXiv [cs.LG] (2024).
Generative models improve fairness of medical classifiers under distribution shifts

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A. & Gowal, S.
Nat. Med. (2024).
Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Martindale, A. P. L., Ng, B., Ngai, V., Kale, A. U., Ferrante di Ruffano, L., Golub, R. M., Collins, G. S., Moher, D., McCradden, M. D., Oakden-Rayner, L., Rivera, S. C., Calvert, M., Kelly, C. J., Lee, C. S., Yau, C., Chan, A.-W., Keane, P. A., Beam, A. L., Denniston, A. K. & Liu, X.
Nat. Commun. 15, 1619 (2024).
Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B. A., Farahani, K., Ferrer, L., Galdran, A., van Ginneken, B., Glocker, B., Godau, P., Hashimoto, D. A., Hoffman, M. M., Huisman, M., Isensee, F., Jannin, P., Kahn, C. E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B. A., Litjens, G., Madani, A., Maier-Hein, K., Martel, A. L., Meijering, E., Menze, B., Moons, K. G. M., Müller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S. M., Rajpoot, N., Reyes, M., Riegler, M. A., Rieke, N., Saez-Rodriguez, J., Sánchez, C. I., Shetty, S., Summers, R. M., Taha, A. A., Tiulpin, A., Tsaftaris, S. A., Van Calster, B., Varoquaux, G., Yaniv, Z. R., Jäger, P. F. & Maier-Hein, L.
Nat. Methods 21, 182–194 (2024).[readcube]

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Detecting shortcut learning for fair medical AI using shortcut testing

Brown, A., Tomasev, N., Freyberg, J., Liu, Y., Karthikesalingam, A. & Schrouff, J.
Nat. Commun. 14, 4314 (2023).
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians

Dvijotham, K., Winkens, J., Barsbey, M., Ghaisas, S., Stanforth, R., Pawlowski, N., Strachan, P., Ahmed, Z., Azizi, S., Bachrach, Y., Culp, L., Daswani, M., Freyberg, J., Kelly, C., Kiraly, A., Kohlberger, T., McKinney, S., Mustafa, B., Natarajan, V., Geras, K., Witowski, J., Qin, Z. Z., Creswell, J., Shetty, S., Sieniek, M., Spitz, T., Corrado, G., Kohli, P., Cemgil, T. & Karthikesalingam, A.
Nat. Med. 1–7 (2023).
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., Mahdavi, S. S., Wulczyn, E., Babenko, B., Walker, M., Loh, A., Chen, P.-H. C., Liu, Y., Bavishi, P., McKinney, S. M., Winkens, J., Roy, A. G., Beaver, Z., Ryan, F., Krogue, J., Etemadi, M., Telang, U., Liu, Y., Peng, L., Corrado, G. S., Webster, D. R., Fleet, D., Hinton, G., Houlsby, N., Karthikesalingam, A., Norouzi, M. & Natarajan, V.
Nature Biomedical Engineering 1–24 (2023). [readcube]

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Diagnosing failures of fairness transfer across distribution shift in real-world medical settings

Schrouff, J., Harris, N., Koyejo, O. O., Alabdulmohsin, I., Schnider, E., Opsahl-Ong, K., Brown, A., Roy, S., Mincu, D., Chen, C., Dieng, A., Liu, Y., Natarajan, V., Karthikesalingam, A., Heller, K. A., Chiappa, S. & D’Amour, A.
NeurIPS (2022).
Comparing human and AI performance in medical machine learning: An open-source Python library for the statistical analysis of reader study data

McKinney, S. M.
medRxiv (2022).
Iterative Quality Control Strategies for Expert Medical Image Labeling

Freeman, B., Hammel, N., Phene, S., Huang, A., Ackermann, R., Kanzheleva, O., Hutson, M., Taggart, C., Duong, Q. & Sayres, R.
HCOMP 9, 60–71 (2021).
Big Self-Supervised Models Advance Medical Image Classification

Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 3478–3488 (2021).
Privacy-first health research with federated learning

Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., MacFarlane, J., Vullikanti, A., Marathe, M., Eastham, P., Brownstein, J. S., Arcas, B. A. Y., Howell, M. D. & Hernandez, J.
NPJ Digit Med 4, 132 (2021).
Supervised Transfer Learning at Scale for Medical Imaging

Mustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Karthikesalingam, A., Houlsby, N. & Natarajan, V.
arXiv [cs.CV] (2021).
Big Self-Supervised Models Advance Medical Image Classification

Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V. & Norouzi, M.
arXiv [eess.IV] (2021).
Underspecification Presents Challenges for Credibility in Modern Machine Learning

D’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.
JMLR. arXiv [cs.LG] (2020).

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Contrastive Training for Improved Out-of-Distribution Detection

Winkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., Cemgil, T., Ali Eslami, S. M. & Ronneberger, O.
arXiv [cs.LG] (2020).
Customization scenarios for de-identification of clinical notes

Hartman, T., Howell, M., Dean, J., Hoory, S., Slyper, R., Laish, I., Gilon, O, Vainstein, D., Corrado, G., Chou, K., Po, M., Williams, J., Ellis, S., Bee, G., Hassidim, A., Amira, R., Beryozkin, G., Szpektor, I., & Matias, Y.
BMC (2020).

Dermatology

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Endoscopy

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Epidemiological Forecasting

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Eye Diseases

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Fitbit

Blog Posts [more at Google Keyword Blog]

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Genomics

Blog Posts [more at DeepVariant Blog]

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Health Sensors and Signals

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Medical Audio

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Medical Records

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MedGemma Technical Report

Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., Chen, J., Mahvar, F., Yatziv, L., Chen, T., Sterling, B., Baby, S. A., Baby, S. M., Lai, J., Schmidgall, S., Yang, L., Chen, K., Bjornsson, P., Reddy, S., Brush, R., Philbrick, K., Hu, H., Yang, H., Tiwari, R., Jansen, S., Singh, P., Liu, Y., Azizi, S., Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Riviere, M., Rouillard, L., Mesnard, T., Cideron, G., Grill, J.-B., Ramos, S., Yvinec, E., Casbon, M., Buchatskaya, E., Alayrac, J.-B., Lepikhin, D., Feinberg, V., Borgeaud, S., Andreev, A., Hardin, C., Dadashi, R., Hussenot, L., Joulin, A., Bachem, O., Matias, Y., Chou, K., Hassidim, A., Goel, K., Farabet, C., Barral, J., Warkentin, T., Shlens, J., Fleet, D., Cotruta, V., Sanseviero, O., Martins, G., Kirk, P., Rao, A., Shetty, S., Steiner, D. F., Kirmizibayrak, C., Pilgrim, R., Golden, D. & Yang, L.
arXiv [cs.AI] (2025).
User-centred design for machine learning in health care: a case study from care management

Seneviratne, M. G., Li, R. C., Schreier, M., Lopez-Martinez, D., Patel, B. S., Yakubovich, A., Kemp, J. B., Loreaux, E., Gamble, P., El-Khoury, K., Vardoulakis, L., Wong, D., Desai, J., Chen, J. H., Morse, K. E., Downing, N. L., Finger, L. T., Chen, M.-J. & Shah, N.
BMJ Health Care Inform 29, (2022).
Boosting the interpretability of clinical risk scores with intervention predictions

Loreaux, E., Yu, K., Kemp, J., Seneviratne, M., Chen, C., Roy, S., Protsyuk, I., Harris, N., D’Amour, A., Yadlowsky, S. & Chen, M.-J.
arXiv [cs.LG] (2022).
Deciphering clinical abbreviations with a privacy protecting machine learning system

Rajkomar, A., Loreaux, E., Liu, Y., Kemp, J., Li, B., Chen, M.-J., Zhang, Y., Mohiuddin, A. & Gottweis, J.
Nat. Commun. 13, 7456 (2022).
Structured understanding of assessment and plans in clinical documentation

Stupp, D., Barequet, R., Lee, I.-C., Oren, E., Feder, A., Benjamini, A., Hassidim, A., Matias, Y., Ofek, E. & Rajkomar, A.
bioRxiv (2022).
Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing

Roy, S., Mincu, D., Loreaux, E., Mottram, A., Protsyuk, I., Harris, N., Xue, Y., Schrouff, J., Montgomery, H., Connell, A., Tomasev, N., Karthikesalingam, A. & Seneviratne, M.
J. Am. Med. Inform. Assoc. (2021).
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Tomašev, N., Harris, N., Baur, S., Mottram, A., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Magliulo, V., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Osborne, T. F., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Seneviratne, M. G., Ledsam, J. R. & Mohamed, S.
Nat. Protoc. 1–23 (2021). [readcube]

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Learning to Select Best Forecast Tasks for Clinical Outcome Prediction

Xue Y, Du N, Mottram A, Seneviratne A, Dai AM.
NeurIPS (2020).
Deep State-Space Generative Model For Correlated Time-to-Event Predictions

Xue Y, Zhou D, Du N, Dai A, Xu Z, Zhang K, Cui C.
KDD (2020).
Graph convolutional transformer: Learning the graphical structure of electronic health records

Choi E, Xu Z, Li Y, Dusenberry MW, Flores G, Xue Y, Dai AM.
AAAI (2020).
Analyzing the role of model uncertainty for electronic health records

Dusenberry MW, Tran D, Choi E, Kemp J, Nixon J, Jerfel G, Heller K, & Dai AM.
ACM CHIL (2020).
Explaining an increase in predicted risk for clinical alerts

Hardt M, Rajkomar A, Flores G, Dai A, Howell M, Corrado G, Cui C, & Hardt M.
ACM CHIL (2020).
Predicting inpatient medication orders from electronic health record data

Rough, K., Dai, A. M., Zhang, K., Xue, Y., Vardoulakis, L. M., Cui, C., Butte, A. J., Howell, M. D. & Rajkomar, A.
Clin. Pharmacol. Ther. 108, 145–154 (2020).
A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R. & Mohamed, S.
Nature 572, 116–119 (2019). [readcube]

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Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions

Connell, A., Montgomery, H., Martin, P., Nightingale, C., Sadeghi-Alavijeh, O., King, D., Karthikesalingam, A., Hughes, C., Back, T., Ayoub, K., Suleyman, M., Jones, G., Cross, J., Stanley, S., Emerson, M., Merrick, C., Rees, G., Laing, C. & Raine, R.
npj Digit Med 2, 67 (2019).
Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs

Connell A., Raine R., Martin P., Barbosa E.C., Morris S., Nightingale C., Sadeghi-Alavijeh O., King D., Karthikesalingam A., Hughes C., Back T., Ayoub K., Suleyman M., Jones G., Cross J., Stanley S., Emerson M., Merrick C., Rees G., Montgomery H., & Laing C.
J Med Internet Res 21(7):e13147 (2019).
Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Connell A, Black G, Montgomery H, Martin P, Nightingale C, King D, Karthikesalingam A, Hughes C, Back T, Ayoub K, Suleyman M, Jones G, Cross J, Stanley S, Emerson M, Merrick C, Rees G, Laing C, & Raine R.
J Med Internet Res 21(7):e13143 (2019).
Improved Patient Classification with Language Model Pretraining Over Clinical Notes

Kemp J, Rajkomar A, & Dai AM.
arXiv [cs.LG] (2019).
Federated and Differentially Private Learning for Electronic Health Records

Pfohl SR, Dai AM, & Heller K.
arXiv [cs.LG] (2019).
Deep Physiological State Space Model for Clinical Forecasting

Xue Y, Zhou D, Du N, Dai AM, Xu Z, Zhang K,& Cui C.
arXiv [cs.LG] (2019).
Modelling EHR timeseries by restricting feature interaction

Zhang K, Xue Y, Flores G, Rajkomar A, Cui C, & Dai AM.
arXiv [cs.LG] (2019).
Scalable and accurate deep learning with electronic health records

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J.
npj Digital Med 1, 18 (2018).

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Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning

Weng, W.-H., Baur, S., Daswani, M., Chen, C., Harrell, L., Kakarmath, S., Jabara, M., Behsaz, B., McLean, C. Y., Matias, Y., Corrado, G. S., Shetty, S., Prabhakara, S., Liu, Y., Danaei, G. & Ardila, D.
PLOS Global Public Health. 4(6): e0003204 (2024).
Using generative AI to investigate medical imagery models and datasets

Lang, O., Yaya-Stupp, D., Traynis, I., Cole-Lewis, H., Bennett, C. R., Lyles, C. R., Lau, C., Irani, M., Semturs, C., Webster, D. R., Corrado, G. S., Hassidim, A., Matias, Y., Liu, Y., Hammel, N. & Babenko, B.
EBioMedicine 102, 105075 (2024).
Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock

Ahadi, S., Wilson, K. A., Jr, Babenko, B., McLean, C. Y., Bryant, D., Pritchard, O., Kumar, A., Carrera, E. M., Lamy, R., Stewart, J. M., Varadarajan, A., Berndl, M., Kapahi, P. & Bashir, A.
Elife 12, (2023).
A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study

Babenko, B., Traynis, I., Chen, C., Singh, P., Uddin, A., Cuadros, J., Daskivich, L. P., Maa, A. Y., Kim, R., Kang, E. Y.-C., Matias, Y., Corrado, G. S., Peng, L., Webster, D. R., Semturs, C., Krause, J., Varadarajan, A. V., Hammel, N. & Liu, Y.
The Lancet Digital Health (2023).
Detection of signs of disease in external photographs of the eyes via deep learning

Babenko, B., Mitani, A., Traynis, I., Kitade, N., Singh, P., Maa, A. Y., Cuadros, J., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A., Hammel, N. & Liu, Y.
Nat Biomed Eng (2022).[readcube]

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Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

Liu, X., Ali, T. K., Singh, P., Shah, A., McKinney, S. M., Ruamviboonsuk, P., Turner, A. W., Keane, P. A., Chotcomwongse, P., Nganthavee, V., Chia, M., Huemer, J., Cuadros, J., Raman, R., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Varadarajan, A. V., Liu, Y., Chopra, R. & Bavishi, P.
Ophthalmol Retina (2022).
Retinal fundus photographs capture hemoglobin loss after blood donation

Mitani, A., Traynis, I., Singh, P., Corrado, G. S., Webster, D. R., Peng, L. H., Varadarajan, A. V., Liu, Y. & Hammel, N.
Predicting the risk of developing diabetic retinopathy using deep learning

Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A. V., Hammel, N., Liu, Y. & Bavishi, P.
The Lancet Digital Health (2020).
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

Moraes, G., Fu, D. J., Wilson, M., Khalid, H., Wagner, S. K., Korot, E., Ferraz, D., Faes, L., Kelly, C. J., Spitz, T., Patel, P. J., Balaskas, K., Keenan, T. D. L., Keane, P. A. & Chopra, R.
Ophthalmology (2020).
Scientific Discovery by Generating Counterfactuals Using Image Translation

Narayanaswamy, A., Venugopalan, S., Webster, D. R., Peng, L., Corrado, G. S., Ruamviboonsuk, P., Bavishi, P., Brenner, M., Nelson, P. C. & Varadarajan, A. V.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 273–283 (2020). doi:10.1007/978-3-030-59710-8_27 [arXiv]

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Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., Moraes, G., Meyer, C., Wilson, M., Dixon, J., Hughes, C., Rees, G., Khaw, P. T., Karthikesalingam, A., King, D., Hassabis, D., Suleyman, M., Back, T., Ledsam, J. R., Keane, P. A. & De Fauw, J.
Nat. Med. (2020). [readcube]

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Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Varadarajan, A. V., Bavishi, P., Ruamviboonsuk, P., Chotcomwongse, P., Venugopalan, S., Narayanaswamy, A., Cuadros, J., Kanai, K., Bresnick, G., Tadarati, M., Silpa-Archa, S., Limwattanayingyong, J., Nganthavee, V., Ledsam, J. R., Keane, P. A., Corrado, G. S., Peng, L. & Webster, D. R.
Nat. Commun. 11, 130 (2020).
Detection of anaemia from retinal fundus images via deep learning

Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y. & Varadarajan, A. V.
Nat Biomed Eng (2019). [readcube]

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Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning

Babenko, B., Balasubramanian, S., Blumer, K. E., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N. & Varadarajan, A. V.
arXiv [cs.CV] (2019).
Deep Learning for Predicting Refractive Error From Retinal Fundus Images

aradarajan, A.V., Poplin, R., Blumer, K., Angermueller, C., Lesdam, J., Chopra, R., Keane, P.A., Corrado, G. S., Peng, L., Webster, D. R.
Invest. Ophthalmol. Vis. Sci. 59, 2861–2868 (2018).
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R.
Nat. Biomed. Eng. 2, 158–164 (2018). [readcube]

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