| Artificial intelligence in healthcare KH Yu, AL Beam, IS Kohane Nature biomedical engineering 2 (10), 719-731, 2018 | 3560* | 2018 |
| Big data and machine learning in health care AL Beam, IS Kohane Jama 319 (13), 1317-1318, 2018 | 2622 | 2018 |
| Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension X Liu, SC Rivera, D Moher, MJ Calvert, AK Denniston bmj 370, 2020 | 1584* | 2020 |
| TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods GS Collins, KGM Moons, P Dhiman, RD Riley, AL Beam, B Van Calster, ... bmj 385, 2024 | 1583 | 2024 |
| The false hope of current approaches to explainable artificial intelligence in health care M Ghassemi, L Oakden-Rayner, AL Beam The lancet digital health 3 (11), e745-e750, 2021 | 1484 | 2021 |
| Adversarial attacks on medical machine learning SG Finlayson, JD Bowers, J Ito, JL Zittrain, AL Beam, IS Kohane Science 363 (6433), 1287-1289, 2019 | 1284 | 2019 |
| Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension S Cruz Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert Nature medicine 26 (9), 1351-1363, 2020 | 1108* | 2020 |
| Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial … GS Collins, P Dhiman, CLA Navarro, J Ma, L Hooft, JB Reitsma, P Logullo, ... BMJ open 11 (7), e048008, 2021 | 778 | 2021 |
| Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study GA Brat, D Agniel, A Beam, B Yorkgitis, M Bicket, M Homer, KP Fox, ... Bmj 360, 2018 | 706 | 2018 |
| A review of challenges and opportunities in machine learning for health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 2020, 191, 2020 | 692* | 2020 |
| Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ... bmj 377, 2022 | 691 | 2022 |
| Illuminating protein space with a programmable generative model JB Ingraham, M Baranov, Z Costello, KW Barber, W Wang, A Ismail, ... Nature 623 (7989), 1070-1078, 2023 | 626 | 2023 |
| Artificial intelligence in health care: The hope, the hype, the promise, the peril M Matheny, ST Israni, M Ahmed, D Whicher National Academies Press 2019, 7-226, 2022 | 540 | 2022 |
| Second opinion needed: communicating uncertainty in medical machine learning B Kompa, J Snoek, AL Beam npj Digital Medicine 4 (1), 1-6, 2021 | 499 | 2021 |
| Challenges to the reproducibility of machine learning models in health care AL Beam, AK Manrai, M Ghassemi Jama 323 (4), 305-306, 2020 | 419 | 2020 |
| Adversarial attacks against medical deep learning systems SG Finlayson, HW Chung, IS Kohane, Beam, A.L. arXiv preprint arXiv:1804.05296, 2018, 2019 | 368* | 2019 |
| Zebrafish developmental screening of the ToxCast™ Phase I chemical library S Padilla, D Corum, B Padnos, DL Hunter, A Beam, KA Houck, N Sipes, ... Reproductive toxicology 33 (2), 174-187, 2012 | 363 | 2012 |
| Artificial intelligence in medicine AL Beam, JM Drazen, IS Kohane, TY Leong, AK Manrai, EJ Rubin New England Journal of Medicine 388 (13), 1220-1221, 2023 | 301 | 2023 |
| Translating artificial intelligence into clinical care AL Beam, IS Kohane Jama 316 (22), 2368-2369, 2016 | 301 | 2016 |
| Clinical concept embeddings learned from massive sources of multimodal medical data AL Beam, B Kompa, A Schmaltz, I Fried, G Weber, N Palmer, X Shi, T Cai, ... Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 25, 295, 2020 | 292 | 2020 |
Isaac KohaneHarvard Medical School, Children's Hospital, Brigham and Women's HospitalVerified email at hms.harvard.edu
