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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.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
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.
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.
