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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. Lee, DJ, Hamghalam, M, Wang, L, Lin, HM, Colak, E, Mamdani, M et al.. The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses. Biomed Eng Online. 2025;24 (1):49. doi: 10.1186/s12938-025-01376-7. PubMed PMID:40289097 PubMed Central PMC12036281.
  2. Khan, H, Girdharry, NR, Massin, SZ, Abu-Raisi, M, Saposnik, G, Mamdani, M et al.. Current Prognostic Biomarkers for Peripheral Arterial Disease: A Comprehensive Systematic Review of the Literature. Metabolites. 2025;15 (4):. doi: 10.3390/metabo15040224. PubMed PMID:40278353 PubMed Central PMC12029480.
  3. Li, B, Aljabri, B, Beaton, D, Al-Omran, L, Hussain, MA, Lee, DS et al.. Predicting outcomes following open abdominal aortic aneurysm repair using machine learning. Sci Rep. 2025;15 (1):14362. doi: 10.1038/s41598-025-98573-0. PubMed PMID:40274999 PubMed Central PMC12022244.
  4. Li, B, Aljabri, B, Beaton, D, Hussain, MA, Lee, DS, Wijeysundera, DN et al.. Predicting Outcomes Following Carotid Artery Stenting Using Machine Learning. J Endovasc Ther. 2025; :15266028251333670. doi: 10.1177/15266028251333670. PubMed PMID:40249213 .
  5. Sung, L, Brudno, M, Caesar, MCW, Verma, AA, Buchsbaum, B, Retnakaran, R et al.. Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions. Front Digit Health. 2025;7 :1511943. doi: 10.3389/fdgth.2025.1511943. PubMed PMID:40161559 PubMed Central PMC11949942.
  6. Almanna, MA, Elkaim, LM, Alvi, MA, Levett, JJ, Li, B, Mamdani, M et al.. Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X. JMIR Form Res. 2025; :. doi: 10.2196/60859. PubMed PMID:40131365 .
  7. Li, B, Eisenberg, N, Beaton, D, Lee, DS, Aljabri, B, Al-Omran, L et al.. Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair. J Am Heart Assoc. 2025;14 (5):e039221. doi: 10.1161/JAHA.124.039221. PubMed PMID:40028848 .
  8. 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.
  9. 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.
  10. 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.
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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