This course focuses on evaluation and governance of AI systems in clinical environments. It complements capability measurement science by examining decision impact, deployment constraints, regulatory oversight, and post-market monitoring in healthcare. Students will critically analyze how AI systems are designed, validated, implemented, and monitored in high-stakes clinical environments, with particular emphasis on bias, uncertainty, transparency, regulatory accountability, and real-world performance. Through case studies, coding assignments (for students taking 3 units), and a capstone evaluation project, students will examine why models that perform well in development often fail in deployment, and how governance and monitoring structures can mitigate risk while promoting equity and safety. This course emphasizes critical evaluation over algorithmic derivation.Course Page: https://biomedin223.su.domains/2026/
2-3 units · Medical Option (Med-Ltr-CR/NC)
This course focuses on evaluation and governance of AI systems in clinical environments. It complements capability measurement science by examining decision impact, deployment constraints, regulatory oversight, and post-market monitoring in healthcare. Students will critically analyze how AI systems are designed, validated, implemented, and monitored in high-stakes clinical environments, with particular emphasis on bias, uncertainty, transparency, regulatory accountability, and real-world performance. Through case studies, coding assignments (for students taking 3 units), and a capstone evaluation project, students will examine why models that perform well in development often fail in deployment, and how governance and monitoring structures can mitigate risk while promoting equity and safety. This course emphasizes critical evaluation over algorithmic derivation.Course Page: https://biomedin223.su.domains/2026/
Offered in Spring 2027 at Stanford University.