{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T07:08:29Z","timestamp":1789110509394,"version":"build-2803163510"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Generative artificial intelligence (AI) is rapidly transforming medical education. While AI enhances personalized learning, its integration raises concerns regarding \u201ccognitive outsourcing,\u201d automation bias, and erosion of independent clinical reasoning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim &amp;amp; methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main findings<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key recommendations<\/jats:title>\n                    <jats:p>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 \u201cAI treats the chart\u201d while \u201chumans treat the patient.\u201d 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 \u201cunlearning\u201d of obsolete heuristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Rather than competing with algorithmic memory, medical education needs to cultivate \u201caugmented clinicians\u201d equipped with high AI literacy and profound humanness, ensuring technology enhances rather than displaces relational patient care.<\/jats:p>\n                  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