Shaping the Future of Medical Education with Generative AI: Student-Initiated Data Science Interest Group Launches ChatClinic (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> The establishment of a medical student-initiated Data Science Interest Group (DSIG) demonstrates the growing interest and integration of data science, artificial intelligence (AI), and medicine. The DSIG works to provide an interdisciplinary environment for student-driven data science exposure and education, centered on four main pillars: interest exploration in data science, data science education and skill development, student and faculty collaborations, and learning from leaders in the field. As a case study, we present "ChatClinic," an AI-based medical education tool created and designed by DSIG members with the goal of simulating patient history taking and clinical decision-making interactions. The creation of ChatClinic serves as a demonstration of the DSIG's four-pillar approach and exemplifies how student interest in AI can lead to advances in the field of medicine. The paper also considers ethical and practical challenges in AI integration into medical education, such as information accuracy, potential over-reliance on AI, and issues with model interpretability. Our hope is that the DSIG and ChatClinic serve as models for institutions seeking to navigate the complex landscape of AI integration into medical education. </sec>

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