Applications of AI in Genetics and Genomics

April 11, 2022 · 1 min read
Abstract
Overview of high-yield applications of AI across genetics and genomics, with emphasis on clinically relevant workflows (e.g., phenotype-driven prioritization, variant interpretation support, and integration of genomic data with clinical context). The lecture highlights common evaluation pitfalls and how to reason about model performance and limitations in translational settings.
presentations

This is the Applications of AI in Genomic Medicine lecture I gave as part of the AI in Medicine Course for the AI in Medicine Student Society (AIMSS) in 2022. This lecture was a part of the Applications of AI in Medicine stream.

Talk Recorded Lecture Teaching AIMSS AI in Medicine Course Applications of AI Genetics Genomics 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.