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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 session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
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.
