Matheny, Michael E.

Artificial Intelligence in Health Care: A Report From the National Academy of Medicine

Matheny, M. E., Whicher, D., & Thadaney Israni, S. (2020) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 628

Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward

Turchin, A., Matheny, M. E., Shubina, M., Scanlon, J. V., Greenwood, B., & Pendergrass, M. L. (2009) · Diabetes Care

Hyperglycemia and glycemic control in critically ill and hospitalized patientsDiabetes Management and Research

Cited by 453

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak, G., Ryan, P. B., Duke, J. D., Shah, N. H., Park, R. W., Huser, V., Suchard, M. A., Schuemie, M. J., DeFalco, F. J., Perotte, A., et al. (2016) · Proceedings of the National Academy of Sciences

Medical Coding and Health InformationMachine Learning in Healthcare

Cited by 326

Calibration drift in regression and machine learning models for acute kidney injury

Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E. (2017) · Journal of the American Medical Informatics Association

Acute Kidney Injury ResearchMachine Learning in Healthcare

Cited by 312

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study

Lane, J. C. E., Weaver, J., Kostka, K., Duarte-Salles, T., Abrahao, M. T. F., Alghoul, H., Alser, O., Alshammari, T. M., Biedermann, P., Banda, J. M., et al. (2020) · The Lancet Rheumatology

Rheumatoid Arthritis Research and TherapiesPharmacovigilance and Adverse Drug Reactions

Cited by 258

Detection of calibration drift in clinical prediction models to inform model updating

Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics

Machine Learning in HealthcareData Stream Mining Techniques

Cited by 160

Utilization Rates of SGLT2 Inhibitors and GLP-1 Receptor Agonists and Their Facility-Level Variation Among Patients With Atherosclerotic Cardiovascular Disease and Type 2 Diabetes: Insights From the Department of Veterans Affairs

Mahtta, D., Ramsey, D. J., Lee, M. T., Chen, L., Al Rifai, M., Akeroyd, J. M., Vaughan, E. M., Matheny, M. E., Santo, K. R. D. E., Navaneethan, S. D., et al. (2022) · Diabetes Care

Diabetes Treatment and ManagementHeart Failure Treatment and Management

Cited by 137

Semaglutide and Nonarteritic Anterior Ischemic Optic Neuropathy

Cai, C. X., Hribar, M., Baxter, S., Goetz, K., Swaminathan, S. S., Flowers, A., Brown, E. N., Toy, B., Xu, B., Chen, J., et al. (2025) · JAMA Ophthalmology

Diabetes Treatment and ManagementDrug-Induced Ocular Toxicity

Cited by 129

Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction

Song, X., Yu, A. S. L., Kellum, J. A., Waitman, L. R., Matheny, M. E., Simpson, S. Q., Hu, Y., & Liu, M. (2020) · Nature Communications

Acute Kidney Injury ResearchMachine Learning in Healthcare

Cited by 120

A proposed framework on integrating health equity and racial justice into the artificial intelligence development lifecycle

Dankwa-Mullan, I., Scheufele, E. L., Matheny, M. E., Quintana, Y., Chapman, W. W., Jackson, G., & South, B. R. (2021) · Journal of Health Care for the Poor and Underserved

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 108

Enhancing trust in AI through industry self-governance.

Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 83

Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study

Prats-Uribe, A., Sena, A. G., Lai, L. Y. H., Ahmed, W.-U.-R., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Carter, W., Casajust, P., et al. (2021) · BMJ

COVID-19 Clinical Research StudiesCOVID-19 and healthcare impacts

Cited by 68

Risk of hypoglycemia following hospital discharge in patients with diabetes and acute kidney injury

Hung, A. M., Siew, E. D., Wilson, O. D., Perkins, A. M., Greevy, R. A., Horner, J., Abdel-Kader, K., Parr, S. K., Roumie, C. L., Griffin, M. R., et al. (2018) · Diabetes Care

Acute Kidney Injury ResearchDiabetes Management and Research

Cited by 49

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes

Khera, R., Aminorroaya, A., Dhingra, L. S., Thangaraj, P. M., Pedroso Camargos, A., Bu, F., Ding, X., Nishimura, A., Anand, T. V., Arshad, F., et al. (2024) · Journal of the American College of Cardiology

Diabetes Treatment and ManagementHeart Failure Treatment and Management

Cited by 47

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study

Khera, R., Aminorroaya, A., Dhingra, L. S., Thangaraj, P. M., Camargos, A. P., Bu, F., Ding, X., Nishimura, A., Anand, T. V., Arshad, F., et al. (2024) · Journal of the American College of Cardiology

Diabetes Treatment and ManagementHeart Failure Treatment and Management

Cited by 46

Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance

Davis, S. E., Embí, P. J., & Matheny, M. E. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 46

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Reeves, R. M., Christensen, L., Brown, J. R., Conway, M., Levis, M., Gobbel, G. T., Shah, R. U., Goodrich, C., Ricket, I., Minter, F., et al. (2021) · Journal of Biomedical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 45

Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability

Davis, S. E., Dorn, C., Park, D. J., & Matheny, M. E. (2025) · Journal of the American Medical Informatics Association

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 43

Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings

Davis, S. E., Walsh, C. G., & Matheny, M. E. (2022) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 33

Equity, Inclusivity, and Innovative Digital Technologies to Improve Adolescent and Young Adult Health

Israni, S. T., Matheny, M. E., Matlow, R., & Whicher, D. (2020) · Journal of Adolescent Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 24

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab

Cai, C. X., Nishimura, A., Bowring, M. G., Westlund, E., Tran, D., Ng, J. H., Nagy, P., Cook, M., McLeggon, J.-A., DuVall, S. L., et al. (2024) · Ophthalmology Retina

Retinal Diseases and TreatmentsDrug-Induced Ocular Toxicity

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