Case-based Learning: Conclusion & Wrap-up

June 5, 2023 · 1 min read
Abstract
This session concludes the AIMSS AI in Medicine course using a case-based approach, focusing on reading and applying medical AI papers. It synthesizes key takeaways, common evaluation pitfalls, and practical heuristics for translating paper claims into clinical reasoning and decision-making.
talks

This is the case-based learning conclusion and wrap-up session, focused on applying medical AI papers and synthesizing key takeaways.

Talk Lecture AIMSS AI in Medicine Course Case-Based Learning Medical AI Artificial Intelligence
Ehsan Misaghi
Authors
Ehsan Misaghi (he/him)
Clinician-Scientist Trainee
Ehsan Misaghi is an MD/PhD Candidate at the University of Alberta working at the intersection of ophthalmology, genetics, and artificial intelligence. His research focuses on inherited retinal disease and genotype–phenotype correlations in ocular disease, with an emphasis on mechanistic insight and translational relevance. Alongside research, he builds and evaluates practical AI tools for clinical and educational settings, and he leads medical AI education, research, and community-building through the AI in Medical Systems Society (AIMSS) and related initiatives. His goal is to advance rigorous, clinically useful research and translate it into improved diagnostics, care pathways, and responsible innovation.