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Muhammad Mamdani

MPH, MA, PharmD

Scientist

Email: Muhammad.Mamdani@unityhealth.to
Tel: 416-864-3037

Research Programs

Biography

Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Education and Research in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute, which is a leading institution for artificial intelligence research in Canada.

Dr. Mamdani holds a Doctor of Pharmacy degree from the University of Michigan, a fellowship in pharmacoeconomics from the Detroit Medical Centre, a Master of Arts degree in econometric theory from Wayne State University, and a Master of Public Health from Harvard University with a focus on statistics and epidemiology. He has previously been named among Canada’s Top 40 under 40. Dr. Mamdani’s research interests include pharmacoepidemiology, pharmacoeconomics, drug policy, and the application of advanced analytics approaches to clinical problems and health policy decision-making. He has published over 500 studies in peer-reviewed healthcare journals.

Please note: Dr. Mamdani is not taking any summer students

Recent Publications

  1. McCradden, MD, London, AJ, Gichoya, JW, Sendak, M, Erdman, L, Stedman, I et al.. CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare. Nat Med. 2025; :. doi: 10.1038/s41591-024-03364-1. PubMed PMID:39762426 .
  2. Aglipay, M, Maguire, JL, Swayze, S, Tuite, A, Mamdani, M, Keown-Stoneman, C et al.. Initial Effectiveness of mRNA-1273 Against SARS-CoV-2 Infection and Hospitalization in Young Children. Open Forum Infect Dis. 2025;12 (1):ofae718. doi: 10.1093/ofid/ofae718. PubMed PMID:39758749 PubMed Central PMC11697100.
  3. Li, B, Eisenberg, N, Beaton, D, Lee, DS, Al-Omran, L, Wijeysundera, DN et al.. Predicting lack of clinical improvement following varicose vein ablation using machine learning. J Vasc Surg Venous Lymphat Disord. 2024; :102162. doi: 10.1016/j.jvsv.2024.102162. PubMed PMID:39732288 .
  4. Jung, JJ, Pou-Prom, C, Mamdani, M. Optimizing patient monitoring to prevent clinical deterioration in surgical wards using machine learning. Am J Surg. 2024; :116138. doi: 10.1016/j.amjsurg.2024.116138. PubMed PMID:39694714 .
  5. Li, X, Kahane, A, Keown-Stoneman, CDG, Omand, JA, Borkhoff, CM, Lebovic, G et al.. Early childhood body mass index growth and school readiness: A longitudinal cohort study. Paediatr Perinat Epidemiol. 2024;38 (8):733-744. doi: 10.1111/ppe.13114. PubMed PMID:39607066 .
  6. Verma, AA, Stukel, TA, Colacci, M, Bell, S, Ailon, J, Friedrich, JO et al.. Clinical evaluation of a machine learning-based early warning system for patient deterioration. CMAJ. 2024;196 (30):E1027-E1037. doi: 10.1503/cmaj.240132. PubMed PMID:39284602 PubMed Central PMC11412734.
  7. Perivolaris, A, Adams-McGavin, C, Madan, Y, Kishibe, T, Antoniou, T, Mamdani, M et al.. Quality of interaction between clinicians and artificial intelligence systems. A systematic review. Future Healthc J. 2024;11 (3):100172. doi: 10.1016/j.fhj.2024.100172. PubMed PMID:39281326 PubMed Central PMC11399614.
  8. Li, B, Beaton, D, Lee, DS, Aljabri, B, Al-Omran, L, Wijeysundera, DN et al.. Comprehensive review of virtual assistants in vascular surgery. Semin Vasc Surg. 2024;37 (3):342-349. doi: 10.1053/j.semvascsurg.2024.07.001. PubMed PMID:39277351 .
  9. D'Amours, G, Clausen, M, Luca, S, Reble, E, Kodida, R, Assamad, D et al.. Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations. BMJ Open. 2024;14 (9):e090084. doi: 10.1136/bmjopen-2024-090084. PubMed PMID:39231549 PubMed Central PMC11407190.
  10. Li, B, Eisenberg, N, Beaton, D, Lee, DS, Al-Omran, L, Wijeysundera, DN et al.. Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting. J Am Heart Assoc. 2024;13 (17):e035425. doi: 10.1161/JAHA.124.035425. PubMed PMID:39189482 PubMed Central PMC11646515.
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Affiliations & Other Activities

  • Scientist, Li Ka Shing Knowledge Institute, St. Michael’s Hospital
  • Professor, Institute of Health Policy, Management, and Evaluation, University of Toronto
  • Professor, Leslie Dan Faculty of Pharmacy, University of Toronto
  • Adjunct Professor, King Saud University Senior Adjunct
  • Scientist, Institute for Clinical Evaluative Sciences