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. Harish, V, Grewal, K, Mamdani, M, Thiruganasambandamoorthy, V. Teaching old tools new tricks-preparing emergency medicine for the impact of machine learning-based risk prediction models. CJEM. 2023; :1-5. doi: 10.1007/s43678-023-00480-8. PubMed PMID:36933121 PubMed Central PMC10024279.
  2. Zipursky, JS, Gomes, T, Everett, K, Calzavara, A, Paterson, JM, Austin, PC et al.. Maternal opioid treatment after delivery and risk of adverse infant outcomes: population based cohort study. BMJ. 2023;380 :e074005. doi: 10.1136/bmj-2022-074005. PubMed PMID:36921977 PubMed Central PMC10015218.
  3. Harish, V, Samson, TG, Diemert, L, Tuite, A, Mamdani, M, Khan, K et al.. Governing partnerships with technology companies as part of the COVID-19 response in Canada: A qualitative case study. PLOS Digit Health. 2022;1 (12):e0000164. doi: 10.1371/journal.pdig.0000164. PubMed PMID:36812643 PubMed Central PMC9931354.
  4. Jacob-Brassard, J, Al-Omran, M, Stukel, TA, Mamdani, M, Lee, DS, Papia, G et al.. The influence of diabetes on temporal trends in lower extremity revascularisation and amputation for peripheral artery disease: A population-based repeated cross-sectional analysis. Diabet Med. 2023; :e15056. doi: 10.1111/dme.15056. PubMed PMID:36721971 .
  5. Jacob-Brassard, J, Al-Omran, M, Stukel, TA, Mamdani, M, Lee, DS, de Mestral, C et al.. Regional variation in lower extremity revascularization and amputation for peripheral artery disease. J Vasc Surg. 2023;77 (4):1127-1136. doi: 10.1016/j.jvs.2022.12.032. PubMed PMID:36681257 .
  6. Antoniou, T, McCormack, D, Kitchen, S, Pajer, K, Gardner, W, Lunsky, Y et al.. Geographic variation and sociodemographic correlates of prescription psychotropic drug use among children and youth in Ontario, Canada: a population-based study. BMC Public Health. 2023;23 (1):85. doi: 10.1186/s12889-022-14677-6. PubMed PMID:36631810 PubMed Central PMC9832754.
  7. Gomes, T, Men, S, Campbell, TJ, Tadrous, M, Mamdani, MM, Paterson, JM et al.. Changing patterns of opioid initiation for pain management in Ontario, Canada: A population-based cross-sectional study. PLoS One. 2022;17 (12):e0278508. doi: 10.1371/journal.pone.0278508. PubMed PMID:36480526 PubMed Central PMC9731435.
  8. Fralick, M, Debnath, M, Pou-Prom, C, O'Brien, P, Perkins, BA, Carson, E et al.. Using real-time machine learning to prevent in-hospital hypoglycemia: a prospective study. Intern Emerg Med. 2023;18 (1):325-328. doi: 10.1007/s11739-022-03148-w. PubMed PMID:36369632 PubMed Central PMC9651871.
  9. Pou-Prom, C, Murray, J, Kuzulugil, S, Mamdani, M, Verma, AA. From compute to care: Lessons learned from deploying an early warning system into clinical practice. Front Digit Health. 2022;4 :932123. doi: 10.3389/fdgth.2022.932123. PubMed PMID:36133802 PubMed Central PMC9483018.
  10. Nurse, KM, Janus, M, Birken, CS, Keown-Stoneman, CDG, Omand, JA, Maguire, JL et al.. Predictive Validity of the Infant Toddler Checklist in Primary Care at the 18-month Visit and School Readiness at 4 to 6 Years. Acad Pediatr. 2023;23 (2):322-328. doi: 10.1016/j.acap.2022.09.004. PubMed PMID:36122830 .
Search PubMed

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