Frontiers in medicine · 2026
Cognitive reshaping and resurgence of humanness: restructuring the medical education continuum in the era of generative AI.
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Liu Ya, Han Lutuo, Che Linlin, Dong Wei, Zhang Ting, Guo Hongwei
Аннотация
BACKGROUND: Generative artificial intelligence (AI) is rapidly transforming medical education. While AI enhances personalized learning, its integration raises concerns regarding "cognitive outsourcing," automation bias, and erosion of independent clinical reasoning.
AIM & METHODS: This narrative review critically evaluates the cognitive and educational impacts of generative AI and proposes structural realignment across the medical education continuum. We synthesized literature from major databases (PubMed, Scopus) focusing on AI's intersection with clinical reasoning and humanistic competencies.
MAIN FINDINGS: We identify three cognitive vulnerabilities: deskilling (loss of diagnostic abilities among advanced learners), never-skilling (failure to develop foundational mental models in junior trainees), and automation bias (uncritical acceptance of machine-generated recommendations). Traditional memory-based assessments, such as multiple-choice questions (MCQs), are increasingly insufficient for evaluating clinical readiness.
KEY RECOMMENDATIONS: Medical education needs to pivot towards methodologies AI cannot replicate, acknowledging that strategies like case-based learning (CBL) and problem-based learning (PBL) are not novel but are now urgently necessitated by AI. We propose a division of labor where "AI treats the chart" while "humans treat the patient." Undergraduate medical education (UME) is encouraged to prioritize pathophysiological reasoning to counter never-skilling; graduate medical education (GME) would benefit from emphasizing human-AI collaboration and deliberate reflection to counter deskilling and automation bias; continuing medical education (CME) may consider facilitating lifelong adaptation and structured "unlearning" of obsolete heuristics.
CONCLUSION: Rather than competing with algorithmic memory, medical education needs to cultivate "augmented clinicians" equipped with high AI literacy and profound humanness, ensuring technology enhances rather than displaces relational patient care.
AIM & METHODS: This narrative review critically evaluates the cognitive and educational impacts of generative AI and proposes structural realignment across the medical education continuum. We synthesized literature from major databases (PubMed, Scopus) focusing on AI's intersection with clinical reasoning and humanistic competencies.
MAIN FINDINGS: We identify three cognitive vulnerabilities: deskilling (loss of diagnostic abilities among advanced learners), never-skilling (failure to develop foundational mental models in junior trainees), and automation bias (uncritical acceptance of machine-generated recommendations). Traditional memory-based assessments, such as multiple-choice questions (MCQs), are increasingly insufficient for evaluating clinical readiness.
KEY RECOMMENDATIONS: Medical education needs to pivot towards methodologies AI cannot replicate, acknowledging that strategies like case-based learning (CBL) and problem-based learning (PBL) are not novel but are now urgently necessitated by AI. We propose a division of labor where "AI treats the chart" while "humans treat the patient." Undergraduate medical education (UME) is encouraged to prioritize pathophysiological reasoning to counter never-skilling; graduate medical education (GME) would benefit from emphasizing human-AI collaboration and deliberate reflection to counter deskilling and automation bias; continuing medical education (CME) may consider facilitating lifelong adaptation and structured "unlearning" of obsolete heuristics.
CONCLUSION: Rather than competing with algorithmic memory, medical education needs to cultivate "augmented clinicians" equipped with high AI literacy and profound humanness, ensuring technology enhances rather than displaces relational patient care.