Precision education in the era of AI: promise, pitfalls, and the data divide

Vollständiger Abstract

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Abstract Combining the power of artificial intelligence (AI) and the clinical data within Electronic Health Records is an innovation that may provide actionable educational insights on learners’ experiences in the clinical learning environment. However, the adoption of such processes highlights significant systemic challenges. The "digital divide" poses a risk of inequity, as institutions with sophisticated data architectures can provide superior precision feedback compared to resource-limited centers. Also, the generalizability of AI models remains a concern, as tools trained on local coding patterns and patient populations may not translate across diverse clinical learning environments without rigorous calibration. Finally, the ability to quantify learners’ clinical exposures raises fundamental questions regarding who defines the "adequacy" of clinical experiences. This commentary articulates how the medical education community must thoughtfully address these policy and technical challenges to help AI reach its potential to enhance physician training.