Artificial Intelligence in UK Medical Education: A Framework for Curriculum Reform (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Artificial intelligence (AI) is increasingly transforming healthcare through improvements in diagnosis, predictive analytics, and workflow optimisation. However, there remains a significant gap in AI training within UK medical education, leaving future clinicians underprepared for AI-driven healthcare environments. </sec> <sec> <title>OBJECTIVE</title> This review investigates global best practices for AI integration into medical education and proposes a structured framework for embedding AI into the UK medical curriculum. It aims to assess current attitudes, highlight existing knowledge gaps, and recommend practical implementation strategies. </sec> <sec> <title>METHODS</title> An analysis of international case studies (e.g., Stanford, University of Toronto, CUHK) was conducted alongside a review of teaching methodologies, stakeholder perspectives, and UK-based surveys to identify core competencies and challenges in AI education. </sec> <sec> <title>RESULTS</title> Effective integration strategies include the use of AI-powered simulations, interdisciplinary collaboration, elective modules, and faculty training. Major barriers include lack of AI-literate educators, insufficient ethical training, and limited infrastructure. Knowledge gaps persist among students and faculty in areas such as algorithmic bias, AI ethics, and clinical decision-making. </sec> <sec> <title>CONCLUSIONS</title> To meet the demands of modern healthcare, the UK medical curriculum must adopt comprehensive AI training. This includes practical exposure, ethical awareness, and stakeholder engagement. Proactive reform will ensure graduates are equipped to critically and ethically apply AI tools in clinical practice. </sec>

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