Macy Foundation Innovation Report Part I: Current Landscape of Artificial Intelligence in Medical Education

Abstract

The rapid emergence of artificial intelligence (AI), including generative large language models, offers transformative opportunities in medical education. This proliferation has generated numerous speculative discussions about AI's promise but has been limited in delivering a comprehensive analysis to distinguish evidence-based utility from hype while identifying context-specific limitations.In this first part of a 2-part innovation report, commissioned by the Josiah Macy Jr. Foundation to inform the discussions at a conference on AI in medical education, the authors synthesize the landscape of AI in medical education, underscoring both its potential advantages and inherent challenges. To map the AI landscape, they reviewed 455 articles that targeted 5 medical education domains: (1) admissions, (2) classroom-based learning and teaching, (3) workplace-based learning and teaching, (4) assessment, feedback, and certification, and (5) program evaluation and research.In admissions, AI-driven strategies facilitated holistic applicant reviews through predictive modeling, natural language processing, and large language model-based chatbots. Preclinical learning benefited from AI-powered virtual patients and curriculum design tools that managed expanding medical knowledge and supported robust student practice. Within clinical learning, AI aided diagnostic and interpretive processes, prompting medical education curricula to demand relevant AI competency and literacy frameworks. A few studies reported that assessment and feedback processes became more efficient through automated grading and advanced analytics, which reduced faculty workload and offered timely, targeted feedback. Program evaluation and research gained additional insights using AI on careers, diversity, and performance metrics of faculty and learners, improving resource allocations and guiding evidence-based approaches.Despite these possibilities, bias in AI algorithms, concerns about transparency, inadequate ethical guidelines, and risks of over-reliance highlighted the need for cautious, informed AI implementation. By mapping AI tasks to medical education applications, the authors provide a framework for understanding and leveraging AI's potential while addressing technical, ethical, and human-factor complexities in this evolving field.

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