Muhammad Mamdani

MPH, MA, PharmD

Scientist

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. Kitchen, SA, Gomes, T, Tadrous, M, Pajer, K, Gardner, W, Lunsky, Y et al.. Association between a publicly funded universal drug program and antipsychotic and antidepressant medication dispensing to children. BMC Pediatr. 2025;25 (1):105. doi: 10.1186/s12887-024-05345-2. PubMed PMID:39923012 PubMed Central PMC11806594.
  2. Li, B, Eisenberg, N, Beaton, D, Lee, DS, Al-Omran, L, Wijeysundera, DN et al.. Using machine learning to predict outcomes following transcarotid artery revascularization. Sci Rep. 2025;15 (1):3924. doi: 10.1038/s41598-024-81625-2. PubMed PMID:39890848 PubMed Central PMC11785798.
  3. Khan, H, Zamzam, A, Shaikh, F, Saposnik, G, Mamdani, M, Qadura, M et al.. Investigating tissue factor pathway inhibitor and other protease and protease inhibitors and their association with major adverse aortic events in patients with abdominal aortic aneurysm. Res Pract Thromb Haemost. 2025;9 (1):102645. doi: 10.1016/j.rpth.2024.102645. PubMed PMID:39816169 PubMed Central PMC11732669.
  4. 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;31 (1):9-11. doi: 10.1038/s41591-024-03364-1. PubMed PMID:39762426 .
  5. 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.
  6. 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;13 (3):102162. doi: 10.1016/j.jvsv.2024.102162. PubMed PMID:39732288 PubMed Central PMC11803835.
  7. 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 .
  8. 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 .
  9. 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.
  10. 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.
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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