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Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
The platform enables educators to create clinical cases aligned with their local curricula, learning goals, languages, and assessment needs, while giving learners repeated opportunities to practice open-ended history-taking, diagnostic reasoning, and communication. The seminar will present the motivation for Clinical Mind AI, its design principles, and how the platform is being used by educators and institutions across countries to support scalable, instructor-centered clinical simulation.
The talk will also discuss Clinical Mind AI as a research infrastructure for studying learning in clinical reasoning tasks. We will describe how learner interactions with AI-simulated patients can generate interpretable evidence about reasoning processes, including how students gather information, update hypotheses, document findings, and respond to feedback. The seminar will focus on broader questions at the intersection of AI, medical education, and learning science: how to build AI systems that preserve instructor agency, support multilingual and cross-institutional implementation, produce actionable insights for educators, and contribute to a more rigorous evidence base for AI-supported health professions education.
