AI-Induced Never-Skilling: A Precautionary Framework for Preserving Clinical Competency in Medical Education

<div> The use of artificial intelligence (AI) tools by learners in healthcare presents both substantial opportunities and significant risks. When deployed appropriately, AI systems have been shown to improve diagnostic accuracy, enhance medication safety, and expand access to specialist expertise. However, early and uncritical reliance on AI during formative training may give rise to a distinct and under-recognized risk: never-skilling. Never-skilling occurs when learners substitute AI-generated outputs for the cognitive effort required to develop foundational clinical reasoning skills. </div> <div> <br> </div> <div> Unlike 'de-skilling' in experienced senior clinicians, never-skilling prevents the initial formation of the foundational cognitive framework needed for clinical reasoning among medical students and early trainees. While direct causal evidence remains limited, preliminary signals from non-clinical studies suggest potential risks warrant early intervention. Never-skilling's impact extends beyond individual competency to threaten global healthcare equity, potentially creating AI-dependent physicians who can only practice in resource-rich settings, and raises questions about how independent competency is verified and documented for medical licensure, workforce readiness, and international physician mobility. </div> <div> <br> </div> <div> To prevent and safeguard against never-skilling, we identify three interconnected challenges: competency acquisition failure when AI bypasses productive struggle, calibration deficits that prevent accurate self-assessment, and metacognitive erosion that threatens professional identity formation. To address these risks, we propose a precautionary three-phase framework for medical education and clinical training: establishing baseline AI-independent clinical competency with mandatory assessment constraints, using adversarial pedagogy to train learners to identify AI errors, and supervised integration of AI. This framework addresses this gap, given the absence of longitudinal data tracking competency development in AI-native learners. </div>