Andrew L. Beam

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Isaac KohaneHarvard Medical School, Children's Hospital, Brigham and Women's HospitalZweryfikowany adres z hms.harvard.edu
Benjamin KompaHarvard UniversityZweryfikowany adres z fas.harvard.edu
Arjun (Raj) ManraiAssistant Professor of Biomedical Informatics at Harvard UniversityZweryfikowany adres z hms.harvard.edu
Samuel G. FinlaysonMD-PhD. Pediatrics-Genetics Resident at UW/Seattle Children'sZweryfikowany adres z seattlechildrens.org
Dr. Marzyeh GhassemiAssociate Professor, EECS/IMES, MITZweryfikowany adres z mit.edu
Gary S. CollinsProfessor of Medical Statistics, Centre for Statistics in Medicine, University of OxfordZweryfikowany adres z csm.ox.ac.uk
Tristan NaumannPrincipal Researcher, Microsoft Research Health FuturesZweryfikowany adres z microsoft.com
Inbar FriedMD-PhD Student, UNC Chapel HillZweryfikowany adres z med.unc.edu
Lauren Oakden-RaynerAustralian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.Zweryfikowany adres z adelaide.edu.au
Anil PalepuPhD Student, Harvard-MIT Health Science & TechnologyZweryfikowany adres z mit.edu
Peter SchulamAmazonZweryfikowany adres z amazon.com
David BellamyFounding AI Scientist | Building an AI for scientific discoveryZweryfikowany adres z fl97inc.com
Jonathan ZittrainGeorge Bemis Prof. of Law, Prof. of Computer Science, and Prof. of Public Policy, Harvard UniversityZweryfikowany adres z law.harvard.edu
Kristyn BeamNeonatologist, Beth Israel Deaconess Medical CenterZweryfikowany adres z bidmc.harvard.edu
Brett Beaulieu-JonesUniversity of ChicagoZweryfikowany adres z uchicago.edu
Bhawesh KumarGoogle ResearchZweryfikowany adres z google.com
Alison Motsinger-ReifNational Institute of Environmental Health SciencesZweryfikowany adres z nih.gov
David ReifChief, Predictive Toxicology, National Institute of Environmental Health SciencesZweryfikowany adres z nih.gov
Allen SchmaltzHarvard UniversityZweryfikowany adres z fas.harvard.edu
Lily PengAppleZweryfikowany adres z apple.com

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Andrew L. Beam

CTO FL97, Inc. | Associate Professor, Harvard University

Zweryfikowany adres z hms.harvard.edu - Strona główna

Artificial Intelligence Machine Learning Medicine


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Artificial intelligence in healthcare KH Yu, AL Beam, IS Kohane Nature biomedical engineering 2 (10), 719-731, 2018	2704	2018
Big data and machine learning in health care AL Beam, IS Kohane Jama 319 (13), 1317-1318, 2018	1927	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	1080*	2020
Adversarial attacks on medical machine learning SG Finlayson, JD Bowers, J Ito, JL Zittrain, AL Beam, IS Kohane Science 363 (6433), 1287-1289, 2019	1030	2019
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	896	2021
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	848*	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	649	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	573	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	495*	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	411	2022
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	411	2019
Second opinion needed: communicating uncertainty in medical machine learning B Kompa, J Snoek, AL Beam npj Digital Medicine 4 (1), 1-6, 2021	365	2021
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	350	2023
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	347	2012
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	337	2024
Challenges to the reproducibility of machine learning models in health care AL Beam, AK Manrai, M Ghassemi Jama 323 (4), 305-306, 2020	331	2020
Adversarial attacks against medical deep learning systems SG Finlayson, HW Chung, IS Kohane, Beam, A.L. arXiv preprint arXiv:1804.05296, 2018, 2019	320*	2019
Translating artificial intelligence into clinical care AL Beam, IS Kohane Jama 316 (22), 2368-2369, 2016	282	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	253	2020
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	232	2020

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