Muhammad Mamdani

MPH, MA, PharmD



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. Kwong, JCC, Khondker, A, Lajkosz, K, McDermott, MBA, Frigola, XB, McCradden, MD et al.. APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open. 2023;6 (9):e2335377. doi: 10.1001/jamanetworkopen.2023.35377. PubMed PMID:37747733 PubMed Central PMC10520738.
  2. Li, B, Aljabri, B, Verma, R, Beaton, D, Eisenberg, N, Lee, DS et al.. Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair. Br J Surg. 2023; :. doi: 10.1093/bjs/znad287. PubMed PMID:37710397 .
  3. Drennan, IR, Thorpe, KE, Scales, D, Cheskes, S, Mamdani, M, Morrison, LJ et al.. Predicting survival post-cardiac arrest: An observational cohort study. Resusc Plus. 2023;15 :100447. doi: 10.1016/j.resplu.2023.100447. PubMed PMID:37662643 PubMed Central PMC10470201.
  4. Li, B, Aljabri, B, Verma, R, Beaton, D, Eisenberg, N, Lee, DS et al.. Using machine learning to predict outcomes following open abdominal aortic aneurysm repair. J Vasc Surg. 2023; :. doi: 10.1016/j.jvs.2023.08.121. PubMed PMID:37634621 .
  5. Khan, H, Shaikh, F, Syed, MH, Mamdani, M, Saposnik, G, Qadura, M et al.. Current Biomarkers for Carotid Artery Stenosis: A Comprehensive Review of the Literature. Metabolites. 2023;13 (8):. doi: 10.3390/metabo13080919. PubMed PMID:37623863 PubMed Central PMC10456624.
  6. Zipursky, JS, Everett, K, Gomes, T, Paterson, JM, Li, P, Austin, PC et al.. Prescription of oxycodone versus codeine after childbirth and risk of persistent opioid use: a population-based cohort study. CMAJ. 2023;195 (29):E973-E983. doi: 10.1503/cmaj.221351. PubMed PMID:37524396 PubMed Central PMC10395796.
  7. Li, B, Verma, R, Beaton, D, Tamim, H, Hussain, MA, Hoballah, JJ et al.. Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning. J Vasc Surg. 2023; :. doi: 10.1016/j.jvs.2023.07.006. PubMed PMID:37454952 .
  8. Gomes, T, Ledlie, S, Tadrous, M, Mamdani, M, Paterson, JM, Juurlink, DN et al.. Trends in Opioid Toxicity-Related Deaths in the US Before and After the Start of the COVID-19 Pandemic, 2011-2021. JAMA Netw Open. 2023;6 (7):e2322303. doi: 10.1001/jamanetworkopen.2023.22303. PubMed PMID:37418260 PubMed Central PMC10329206.
  9. Li, B, Verma, R, Beaton, D, Tamim, H, Hussain, MA, Hoballah, JJ et al.. Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning. Ann Surg. 2023; :. doi: 10.1097/SLA.0000000000005978. PubMed PMID:37389890 .
  10. Antoniou, T, Wang, T, Pajer, K, Gardner, W, Lunsky, Y, Penner, M et al.. Adherence to antipsychotic laboratory monitoring guidelines in children and youth: a population-based study. Front Psychiatry. 2023;14 :1172559. doi: 10.3389/fpsyt.2023.1172559. PubMed PMID:37252150 PubMed Central PMC10217777.
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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