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. Gunter, D, Puac-Polanco, P, Miguel, O, Thornhill, RE, Yu, AYX, Liu, ZA et al.. Rule-based natural language processing for automation of stroke data extraction: a validation study. Neuroradiology. 2022; :. doi: 10.1007/s00234-022-03029-1. PubMed PMID:35913525 .
  2. Omand, JA, Li, X, Keown-Stoneman, CDG, Borkhoff, CM, Duku, E, Lebovic, G et al.. Body Weight at Age Four Years and Readiness to Start School: A Prospective Cohort Study. Child Obes. 2022; :. doi: 10.1089/chi.2022.0018. PubMed PMID:35834646 .
  3. Syed, MH, Al-Omran, M, Ray, JG, Mamdani, M, de Mestral, C. High-Intensity Hospital Utilization Among Adults With Diabetic Foot Ulcers: A Population-based Study. Can J Diabetes. 2022;46 (4):330-336.e7. doi: 10.1016/j.jcjd.2021.10.005. PubMed PMID:35527204 .
  4. Gomes, T, McCormack, D, Bozinoff, N, Tadrous, M, Antoniou, T, Munro, C et al.. Duration of use and outcomes among people with opioid use disorder initiating methadone and buprenorphine in Ontario: a population-based propensity-score matched cohort study. Addiction. 2022;117 (7):1972-1981. doi: 10.1111/add.15862. PubMed PMID:35257434 PubMed Central PMC9313829.
  5. Verma, AA, Murray, J, Greiner, R, Cohen, JP, Shojania, KG, Ghassemi, M et al.. Implementing machine learning in medicine. CMAJ. 2021;193 (34):E1351-E1357. doi: 10.1503/cmaj.202434. PubMed PMID:35213323 PubMed Central PMC8432320.
  6. Vanderloo, LM, Janus, M, Omand, JA, Keown-Stoneman, CDG, Borkhoff, CM, Duku, E et al.. Children's screen use and school readiness at 4-6 years: prospective cohort study. BMC Public Health. 2022;22 (1):382. doi: 10.1186/s12889-022-12629-8. PubMed PMID:35197009 PubMed Central PMC8864975.
  7. Wettstein, MS, Baxter, NN, Sutradhar, R, Mamdani, M, Song, P, Qadri, SR et al.. Uptake of re-resection in T1 bladder cancer: An interrupted population-based time series analysis among different groups of surgeons. Urol Oncol. 2022;40 (4):165.e1-165.e8. doi: 10.1016/j.urolonc.2021.12.006. PubMed PMID:35135701 .
  8. Li, B, Feridooni, T, Cuen-Ojeda, C, Kishibe, T, de Mestral, C, Mamdani, M et al.. Machine learning in vascular surgery: a systematic review and critical appraisal. NPJ Digit Med. 2022;5 (1):7. doi: 10.1038/s41746-021-00552-y. PubMed PMID:35046493 PubMed Central PMC8770468.
  9. Yang, Z, Pou-Prom, C, Jones, A, Banning, M, Dai, D, Mamdani, M et al.. Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study. JMIR Med Inform. 2022;10 (1):e25157. doi: 10.2196/25157. PubMed PMID:35019849 PubMed Central PMC8792771.
  10. Syed, MH, Al-Omran, M, Jacob-Brassard, J, Ray, JG, Hussain, MA, Mamdani, M et al.. ICD-10 Diagnostic Coding for Identifying Hospitalizations Related to a Diabetic Foot Ulcer. Clin Invest Med. 2021;44 (4):E11-16. doi: 10.25011/cim.v44i4.37592. PubMed PMID:34978770 .
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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