Rajendra Aldis, MD
Artificial Intelligence in Healthcare
1-2 pm ET
Presented over Google Meet
Topic
Artificial Intelligence in Healthcare
Presenter Bio
Rajendra Aldis, MD is the Associate Medical Director of Research Informatics at Cambridge Health Alliance (CHA) and an Instructor in Psychiatry at Harvard Medical School. He provides direct patient care as a board certified psychiatrist in CHA’s Primary Care Integrated Behavioral Health Program. As a physician informaticist in the CHA IT department, he has a leadership role in developing CHA’s research informatics and predictive analytics capacity. Dr. Aldis is also a clinician researcher in the CHA Health Equity Research Lab. where his research interests include the application of machine learning and electronic health record data to assess and mitigate health disparities. Dr. Aldis received his MD from Dartmouth Medical School and a Master of Science in Computer Science from Northeastern University. He completed residency in adult psychiatry at Cambridge Health Alliance/Harvard Medical School and completed his fellowship training in global mental health delivery at Harvard Medical School/Partners in Health.
Learning Objectives
Describe AI and its subtypes
Describe the uses of AI in healthcare
Identify the challenges of AI in healthcare
Evaluate the suitability of an AI model for deployment in a healthcare setting
Additional resources (pre-readings, readings, links, etc.)
Kocak, B., Kus, E. A., & Kilickesmez, O. (2021). How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. European Radiology, 31(4), 1819–1830. https://doi.org/10.1007/s00330-020-07324-4
Grzenda, A., Kraguljac, N. V., McDonald, W. M., Nemeroff, C., Torous, J., Alpert, J. E., … Widge, A. S. (2021). Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists. American Journal of Psychiatry, appi.ajp.2020.2. https://doi.org/10.1176/appi.ajp.2020.20030250
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (American Association for the Advancement of Science), 366(6464), 447–453. https://doi.org/10.1126/science.aax2342