Beam, Andrew L.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

Artificial intelligence in healthcare

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 2,817

Big Data and Machine Learning in Health Care

Beam, A. L., & Kohane, I. S. (2018) · JAMA

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 2,506

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 1,724

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 1,421

Adversarial attacks on medical machine learning

Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019) · Science

Adversarial Robustness in Machine LearningArtificial Intelligence in Healthcare and Education

Cited by 1,088

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 933

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 intelligence

Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 852

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study

Brat, G. A., Agniel, D., Beam, A., Yorkgitis, B., Bicket, M., Homer, M., Fox, K. P., Knecht, D. B., McMahill-Walraven, C. N., Palmer, N., et al. (2018) · BMJ

Opioid Use Disorder TreatmentPain Management and Opioid Use

Cited by 591

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 555

Second opinion needed: Communicating uncertainty in medical machine learning

Kompa, B., Snoek, J., & Beam, A. L. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 514

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020) · The Lancet Digital Health

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 486

Illuminating protein space with a programmable generative model

Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 444

Challenges to the Reproducibility of Machine Learning Models in Health Care

Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020) · JAMA

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 366

Artificial Intelligence in Medicine

Beam, A. L., Drazen, J. M., Kohane, I. S., Leong, T.-Y., Manrai, A. K., & Rubin, E. J. (2023) · New England Journal of Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 303

Time to reality check the promises of machine learning-powered precision medicine.

Wilkinson, J., Arnold, K. F., Murray, E. J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., et al. (2020) · The Lancet Digital Health

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 233

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study

Levine, D. M., Tuwani, R., Kompa, B., Varma, A., Finlayson, S. G., Mehrotra, A., & Beam, A. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 199

The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model

Levine, D. M., Tuwani, R., Kompa, B., Varma, A., Finlayson, S. G., Mehrotra, A., & Beam, A. (2023) · medRxiv

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 106

Large Language Models in Mental Health Care: A Scoping Review

Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., et al. (2025) · Current Treatment Options in Psychiatry

Artificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

Cited by 92

Association of Sex With Recurrence of Autism Spectrum Disorder Among Siblings

Palmer, N., Beam, A., Agniel, D., Eran, A., Manrai, A., Spettell, C., Steinberg, G., Mandl, K., Fox, K., Nelson, S. F., et al. (2017) · JAMA Pediatrics

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 89

Illuminating protein space with a programmable generative model

Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 83

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., di Ruffano, L. F., Golub, R. M., Collins, G. S., Moher, D., et al. (2024) · Nature Communications

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 58