Andrew L. Beam
Andrew L. Beam
Assistant Professor, Harvard University
Verified email at - Homepage
Cited by
Cited by
Artificial intelligence in healthcare
KH Yu, AL Beam, IS Kohane
Nature biomedical engineering 2 (10), 719-731, 2018
Big data and machine learning in health care
AL Beam, IS Kohane
Jama 319 (13), 1317-1318, 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
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
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
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
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
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
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
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 10, 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
Second opinion needed: communicating uncertainty in medical machine learning
B Kompa, J Snoek, AL Beam
npj Digital Medicine 4 (1), 1-6, 2021
Challenges to the reproducibility of machine learning models in health care
AL Beam, AK Manrai, M Ghassemi
Jama 323 (4), 305-306, 2020
Adversarial attacks against medical deep learning systems
SG Finlayson, HW Chung, IS Kohane, Beam, A.L.
arXiv preprint arXiv:1804.05296, 2018, 2019
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
Translating artificial intelligence into clinical care
AL Beam, IS Kohane
Jama 316 (22), 2368-2369, 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 2020, 295-306, 2019
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
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
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
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