Bridging the AI Gap: Evaluation of the AI 4 Healthcare Learning Modules as a Tool for Medical Students (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> Machine Learning (ML) or Artificial Intelligence (AI) concepts are not taught in most medical school curricula. However, as the landscape of healthcare changes to include more ML- and AI-based tools to support clinical decision-making, doctors will require a basic understanding of ML and AI concepts to provide patient care practically and ethically using these tools. To better understand how students learn about these concepts today, we recruited 34 current medical student participants to complete an online course through AI4Healthcare.org. This organization provides introductory courses about ML and AI that are targeted toward the medical audience. We surveyed participants before and after the course to characterize their motivations, approaches, and format preferences for learning about basic ML and AI concepts. We found that participants were motivated by personal interest and career planning, and they preferred flexible, interactive formats for learning this material. However, we also observed that an unexpectedly small percentage of the intended medical student audience enrolled in this study. Ultimately, this underscores a critical discontent in current medical education: the authors see learning about AI and ML as potentially career-changing, but only a small percentage of medical students are opting into extracurricular AI-related learning opportunities. If it is imperative for medical students and future physicians to adapt by learning essential AI skills, failing to integrate such content into the standard medical school curriculum may place the majority of students at a significant disadvantage in the long run. </sec>

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