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This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the\n                    <jats:italic>Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills<\/jats:italic>\n                    (PACES-MRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human\u2013AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the\n                    <jats:italic>Second Singularity<\/jats:italic>\n                    -a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI\u2019s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.\n                  <\/jats:p>","DOI":"10.1007\/s10462-025-11352-1","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T07:21:14Z","timestamp":1756279274000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond"],"prefix":"10.1007","volume":"58","author":[{"given":"Chiara","family":"Natali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Marconi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslye Denisse","family":"Dias Duran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Cabitza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"issue":"1","key":"11352_CR1","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.jmir.2022.11.016","volume":"54","author":"TN Akudjedu","year":"2023","unstructured":"Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C (2023) Knowledge, perceptions, and expectations of artificial intelligence in radiography practice: a global radiography workforce survey. 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