Andrew L. Beam

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Isaac KohaneHarvard Medical School, Children's Hospital, Brigham and Women's HospitalVerified email at hms.harvard.edu
Benjamin KompaHarvard UniversityVerified email at fas.harvard.edu
Samuel G. FinlaysonMD-PhD. Pediatrics-Genetics Resident at UW/Seattle Children'sVerified email at seattlechildrens.org
Dr. Marzyeh GhassemiAssistant Professor, EECS.CSAIL/IMES, MITVerified email at mit.edu
Inbar FriedMD-PhD Student, UNC Chapel HillVerified email at med.unc.edu
Tristan NaumannPrincipal Researcher, Microsoft Research Health FuturesVerified email at microsoft.com
Peter SchulamAmazon AlexaVerified email at amazon.com
Jonathan ZittrainGeorge Bemis Professor of Law and Professor of Computer Science, Harvard UniversityVerified email at law.harvard.edu
David ReifChief, Predictive Toxicology Branch, National Institute of Environmental Health SciencesVerified email at nih.gov
Alison Motsinger-ReifNational Institute of Environmental Health SciencesVerified email at nih.gov
Gary S. CollinsProfessor of Medical Statistics, Centre for Statistics in Medicine, University of OxfordVerified email at csm.ox.ac.uk
Brett Beaulieu-JonesUniversity of ChicagoVerified email at uchicago.edu
Arjun (Raj) ManraiAssistant Professor of Biomedical Informatics at Harvard UniversityVerified email at hms.harvard.edu
Allen SchmaltzHarvard UniversityVerified email at fas.harvard.edu
Lauren Oakden-RaynerAustralian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.Verified email at adelaide.edu.au
Lily PengGoogle ResearchVerified email at google.com
Kenneth D. MandlProfessor, Harvard Med. Director, Computational Health Informatics Program, Boston Children'sVerified email at harvard.edu
Jasper SnoekGoogle BrainVerified email at google.com
Maha FarhatAssociate Professor Harvard Medical SchoolVerified email at partners.org
John ZupancicHarvard Medical SchoolVerified email at bidmc.harvard.edu

Andrew L. Beam

Assistant Professor, Harvard University

Verified email at hms.harvard.edu - Homepage

Artificial Intelligence Machine Learning Perinatology Neonatology


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Artificial intelligence in healthcare KH Yu, AL Beam, IS Kohane Nature biomedical engineering 2 (10), 719-731, 2018	1484	2018
Big data and machine learning in health care AL Beam, IS Kohane Jama 319 (13), 1317-1318, 2018	1195	2018
Adversarial attacks on medical machine learning SG Finlayson, JD Bowers, J Ito, JL Zittrain, AL Beam, IS Kohane Science 363 (6433), 1287-1289, 2019	640	2019
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	534*	2020
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	521	2018
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	443*	2020
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	328	2021
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	300	2012
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	259*	2020
Artificial intelligence in health care: The hope, the hype, the promise, the peril M Matheny, ST Israni, M Ahmed, D Whicher Washington, DC: National Academy of Medicine, 2019	229	2019
Adversarial attacks against medical deep learning systems SG Finlayson, HW Chung, IS Kohane, Beam, A.L. arXiv preprint arXiv:1804.05296, 2018, 2019	224*	2019
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	196	2021
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 2020, 295-306, 2019	190*	2019
Challenges to the reproducibility of machine learning models in health care AL Beam, AK Manrai, M Ghassemi Jama 323 (4), 305-306, 2020	189	2020
Translating artificial intelligence into clinical care AL Beam, IS Kohane Jama 316 (22), 2368-2369, 2016	188	2016
Second opinion needed: communicating uncertainty in medical machine learning B Kompa, J Snoek, AL Beam npj Digital Medicine 4 (1), 1-6, 2021	154	2021
Time to reality check the promises of machine learning-powered precision medicine J Wilkinson, KF Arnold, EJ Murray, M van Smeden, K Carr, R Sippy, ... The Lancet Digital Health 2 (12), e677-e680, 2020	137	2020
Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008–2016 AL Beam, I Fried, N Palmer, D Agniel, G Brat, K Fox, I Kohane, A Sinaiko, ... Journal of Perinatology 40 (7), 1091-1099, 2020	96	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, ... Nature medicine 28 (5), 924-933, 2022	83	2022
Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction ML Chen, A Doddi, J Royer, L Freschi, M Schito, M Ezewudo, IS Kohane, ... EBioMedicine 43, 356-369, 2019	81*	2019

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