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Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.

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Τίτλος: Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.
Συγγραφείς: Park Y; Department of Dental Education, School of Dentistry and Dental Research Institute, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2-740-8688., Han Y; Department of Medical Education and Humanities, College of Medicine, Yeungnam University, Daegu, Republic of Korea., Kim H; Department of Curriculum and Instruction, College of Education, University of Illinois, Urbana-Champaign, IL, United States., Ahn S; Dental Research Institute, Seoul National University, Seoul, Republic of Korea., Kron FW; Department of Family and Community Medicine, Macon and Joan Brock Virginia Health Sciences at Old Dominion University, Norfolk, VA, United States., Lee J; Department of Dental Education, School of Dentistry and Dental Research Institute, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2-740-8688.
Πηγή: JMIR medical education [JMIR Med Educ] 2026 Sep 08; Vol. 12, pp. e97608. Date of Electronic Publication: 2026 Sep 08.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101684518 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-3762 (Electronic) Linking ISSN: 23693762 NLM ISO Abbreviation: JMIR Med Educ Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto, ON : JMIR Publications, [2015]-
Ιατρικοί όροι (MeSH): Curriculum*/trends , Curriculum*/standards , Artificial Intelligence*/standards , Professional Competence*/standards , Health Occupations*/education, Humans ; Qualitative Research
Περίληψη: Background: As AI fundamentally transforms the healthcare landscape, medical education curricula have struggled to keep pace with these technological shifts. While current research has established a foundation for general AI literacy, less attention has been given to the role-specific competencies required for the diverse functions that healthcare professionals perform in an AI-integrated environment. Although existing tiered and domain-based competency models have clarified general AI competency requirements, they offer limited guidance on how such competencies should be differentiated according to the roles healthcare professionals perform in practice.
Objective: This study aimed to explore how AI-driven changes in healthcare shape competency requirements across professional roles and to develop differentiated competency frameworks and curriculum implications for 3 distinct roles: users, developers, and leaders.
Methods: Using a qualitative research design, we conducted in-depth interviews with 13 subject matter experts across academic medicine and dentistry, clinical practice, and the healthcare AI industry, who were recruited through 4 independent channels. Data were analyzed using reflexive thematic analysis with inductive coding organized around 3 research questions and published competency frameworks serving as sensitizing concepts. Trustworthiness was supported through investigator triangulation, member checking, an audit trail, and attention to researcher reflexivity and positionality.
Results: We identified a role-differentiated competency framework with a layered structure as follows: (1) Users require machine learning and data literacy to understand where algorithms fail and scrutinize data origins and biases; the ability to select best-fit AI solutions and set appropriate thresholds for human-machine delegation; and a high degree of AI-related professionalism to work responsibly with AI by recognizing its limits, critically appraising outputs, maintaining clinical accountability, preserving patient-centered care, and updating knowledge continuously; (2) Developers add operational competencies, including validating systems across technical performance, clinical relevance, and regulatory compliance; bridging the language gap between clinical needs and technical refinements; and fostering a challenging, problem-solving mindset to address real-world clinical bottlenecks; and (3) Leaders focus on system-level competencies, including macro-level strategic planning, governing medical AI across its full lifecycle-selection, validation, deployment, and monitoring-through policy "roads" and governance frameworks, and optimizing systemic resource utilization while orchestrating trust-based collaboration that safeguards care quality. Rather than a strictly cumulative hierarchy, the roles represent analytic distinctions with shared foundational competencies. This framework translates these competencies into corresponding curriculum implications across the health professions education continuum.
Conclusions: This study identifies role-differentiated AI competencies and proposes a corresponding curriculum scaffold for healthcare education. As a preliminary, hypothesis-generating starting point requiring multistakeholder validation, the proposed framework can help prepare the future healthcare workforce to utilize AI responsibly and to govern and lead the next generation of digital health innovation.
(© Yuyi Park, Yejin Han, Hyeongjo Kim, Solmoe Ahn, Frederick William Kron, Jihyun Lee. Originally published in JMIR Medical Education (https://mededu.jmir.org).)
References: Front Med (Lausanne). 2024 Jan 08;10:1337335. (PMID: 38259835)
Front Dent Med. 2023 Feb 20;4:1085251. (PMID: 39935549)
JMIR Med Educ. 2021 Dec 13;7(4):e31043. (PMID: 34898458)
JMIR Med Educ. 2019 Dec 3;5(2):e16048. (PMID: 31793895)
JMIR Med Educ. 2022 Jun 7;8(2):e35587. (PMID: 35671077)
JMIR Med Educ. 2025 Apr 16;11:e63602. (PMID: 40239213)
Int J Transgend Health. 2022 Oct 25;24(1):1-6. (PMID: 36713144)
Comput Biol Med. 2024 Sep;179:108917. (PMID: 39059212)
Acad Med. 2014 Sep;89(9):1245-51. (PMID: 24979285)
J Med Internet Res. 2025 Aug 26;27:e74187. (PMID: 40857713)
Bull World Health Organ. 2020 Apr 1;98(4):251-256. (PMID: 32284648)
JMIR Form Res. 2025 Mar 26;9:e64266. (PMID: 40138691)
Med Sci Educ. 2025 Jun 4;35(6):2681-2686. (PMID: 41798358)
Eur Radiol. 2021 Aug;31(8):6021-6029. (PMID: 33587154)
Med Teach. 2020 Aug;42(8):846-854. (PMID: 32356468)
Acad Med. 2024 May 1;99(5):524-533. (PMID: 38207056)
Lancet Gastroenterol Hepatol. 2025 Oct;10(10):896-903. (PMID: 40816301)
EClinicalMedicine. 2024 Dec 06;79:102968. (PMID: 39720600)
JMIR Med Educ. 2024 Jun 12;10:e58355. (PMID: 38989834)
JMIR Med Educ. 2024 Jul 18;10:e54793. (PMID: 39023999)
Radiol Artif Intell. 2022 Aug 17;4(5):e220074. (PMID: 36204540)
JMIR Med Inform. 2022 Nov 16;10(11):e37478. (PMID: 36318697)
PeerJ. 2024 Jul 19;12:e17793. (PMID: 39040936)
NPJ Digit Med. 2025 Mar 11;8(1):154. (PMID: 40069303)
JMIR Med Educ. 2024 Feb 20;10:e46500. (PMID: 38376896)
JMIR Med Educ. 2025 Apr 25;11:e66828. (PMID: 40279148)
Med Teach. 2024 Apr;46(4):446-470. (PMID: 38423127)
JMIR Med Educ. 2026 May 06;12:e89815. (PMID: 42090298)
Acad Med. 2023 Mar 1;98(3):348-356. (PMID: 36731054)
Soc Sci Med. 2022 Mar;296:114782. (PMID: 35152047)
Med Educ Online. 2025 Dec 31;30(1):2585637. (PMID: 41327942)
Biomed Eng Lett. 2025 Nov 1;15(6):1073-1083. (PMID: 41280148)
Br Med Bull. 2025 Jan 16;153(1):. (PMID: 39821209)
Med Teach. 2021 Oct;43(10):1139-1148. (PMID: 34344274)
Front Med (Lausanne). 2025 Jan 08;11:1522554. (PMID: 39845830)
Qual Health Res. 2016 Nov;26(13):1753-1760. (PMID: 26613970)
Cell Rep Med. 2023 Oct 17;4(10):101230. (PMID: 37852174)
Med Teach. 2021 Jul;43(7):758-764. (PMID: 34061700)
Contributed Indexing: Keywords: AI; artificial intelligence; competency-based education; curriculum; healthcare professionals; medical education; professional competence; qualitative research
Entry Date(s): Date Created: 20260908 Date Completed: 20260908 Latest Revision: 20260921
Update Code: 20260921
PubMed Central ID: PMC13552830
DOI: 10.2196/97608
PMID: 42710030
Βάση Δεδομένων: MEDLINE
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  Data: Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.
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  Data: <searchLink fieldCode="AU" term="%22Park+Y%22">Park Y</searchLink>; Department of Dental Education, School of Dentistry and Dental Research Institute, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2-740-8688.<br /><searchLink fieldCode="AU" term="%22Han+Y%22">Han Y</searchLink>; Department of Medical Education and Humanities, College of Medicine, Yeungnam University, Daegu, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+H%22">Kim H</searchLink>; Department of Curriculum and Instruction, College of Education, University of Illinois, Urbana-Champaign, IL, United States.<br /><searchLink fieldCode="AU" term="%22Ahn+S%22">Ahn S</searchLink>; Dental Research Institute, Seoul National University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kron+FW%22">Kron FW</searchLink>; Department of Family and Community Medicine, Macon and Joan Brock Virginia Health Sciences at Old Dominion University, Norfolk, VA, United States.<br /><searchLink fieldCode="AU" term="%22Lee+J%22">Lee J</searchLink>; Department of Dental Education, School of Dentistry and Dental Research Institute, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2-740-8688.
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  Data: Background: As AI fundamentally transforms the healthcare landscape, medical education curricula have struggled to keep pace with these technological shifts. While current research has established a foundation for general AI literacy, less attention has been given to the role-specific competencies required for the diverse functions that healthcare professionals perform in an AI-integrated environment. Although existing tiered and domain-based competency models have clarified general AI competency requirements, they offer limited guidance on how such competencies should be differentiated according to the roles healthcare professionals perform in practice.<br />Objective: This study aimed to explore how AI-driven changes in healthcare shape competency requirements across professional roles and to develop differentiated competency frameworks and curriculum implications for 3 distinct roles: users, developers, and leaders.<br />Methods: Using a qualitative research design, we conducted in-depth interviews with 13 subject matter experts across academic medicine and dentistry, clinical practice, and the healthcare AI industry, who were recruited through 4 independent channels. Data were analyzed using reflexive thematic analysis with inductive coding organized around 3 research questions and published competency frameworks serving as sensitizing concepts. Trustworthiness was supported through investigator triangulation, member checking, an audit trail, and attention to researcher reflexivity and positionality.<br />Results: We identified a role-differentiated competency framework with a layered structure as follows: (1) Users require machine learning and data literacy to understand where algorithms fail and scrutinize data origins and biases; the ability to select best-fit AI solutions and set appropriate thresholds for human-machine delegation; and a high degree of AI-related professionalism to work responsibly with AI by recognizing its limits, critically appraising outputs, maintaining clinical accountability, preserving patient-centered care, and updating knowledge continuously; (2) Developers add operational competencies, including validating systems across technical performance, clinical relevance, and regulatory compliance; bridging the language gap between clinical needs and technical refinements; and fostering a challenging, problem-solving mindset to address real-world clinical bottlenecks; and (3) Leaders focus on system-level competencies, including macro-level strategic planning, governing medical AI across its full lifecycle-selection, validation, deployment, and monitoring-through policy "roads" and governance frameworks, and optimizing systemic resource utilization while orchestrating trust-based collaboration that safeguards care quality. Rather than a strictly cumulative hierarchy, the roles represent analytic distinctions with shared foundational competencies. This framework translates these competencies into corresponding curriculum implications across the health professions education continuum.<br />Conclusions: This study identifies role-differentiated AI competencies and proposes a corresponding curriculum scaffold for healthcare education. As a preliminary, hypothesis-generating starting point requiring multistakeholder validation, the proposed framework can help prepare the future healthcare workforce to utilize AI responsibly and to govern and lead the next generation of digital health innovation.<br /> (© Yuyi Park, Yejin Han, Hyeongjo Kim, Solmoe Ahn, Frederick William Kron, Jihyun Lee. Originally published in JMIR Medical Education (https://mededu.jmir.org).)
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  Data: Front Med (Lausanne). 2024 Jan 08;10:1337335. (PMID: <searchLink fieldCode="PM" term="%2238259835%22">38259835)</searchLink><br />Front Dent Med. 2023 Feb 20;4:1085251. (PMID: <searchLink fieldCode="PM" term="%2239935549%22">39935549)</searchLink><br />JMIR Med Educ. 2021 Dec 13;7(4):e31043. (PMID: <searchLink fieldCode="PM" term="%2234898458%22">34898458)</searchLink><br />JMIR Med Educ. 2019 Dec 3;5(2):e16048. (PMID: <searchLink fieldCode="PM" term="%2231793895%22">31793895)</searchLink><br />JMIR Med Educ. 2022 Jun 7;8(2):e35587. (PMID: <searchLink fieldCode="PM" term="%2235671077%22">35671077)</searchLink><br />JMIR Med Educ. 2025 Apr 16;11:e63602. (PMID: <searchLink fieldCode="PM" term="%2240239213%22">40239213)</searchLink><br />Int J Transgend Health. 2022 Oct 25;24(1):1-6. (PMID: <searchLink fieldCode="PM" term="%2236713144%22">36713144)</searchLink><br />Comput Biol Med. 2024 Sep;179:108917. (PMID: <searchLink fieldCode="PM" term="%2239059212%22">39059212)</searchLink><br />Acad Med. 2014 Sep;89(9):1245-51. (PMID: <searchLink fieldCode="PM" term="%2224979285%22">24979285)</searchLink><br />J Med Internet Res. 2025 Aug 26;27:e74187. (PMID: <searchLink fieldCode="PM" term="%2240857713%22">40857713)</searchLink><br />Bull World Health Organ. 2020 Apr 1;98(4):251-256. (PMID: <searchLink fieldCode="PM" term="%2232284648%22">32284648)</searchLink><br />JMIR Form Res. 2025 Mar 26;9:e64266. (PMID: <searchLink fieldCode="PM" term="%2240138691%22">40138691)</searchLink><br />Med Sci Educ. 2025 Jun 4;35(6):2681-2686. (PMID: <searchLink fieldCode="PM" term="%2241798358%22">41798358)</searchLink><br />Eur Radiol. 2021 Aug;31(8):6021-6029. (PMID: <searchLink fieldCode="PM" term="%2233587154%22">33587154)</searchLink><br />Med Teach. 2020 Aug;42(8):846-854. (PMID: <searchLink fieldCode="PM" term="%2232356468%22">32356468)</searchLink><br />Acad Med. 2024 May 1;99(5):524-533. (PMID: <searchLink fieldCode="PM" term="%2238207056%22">38207056)</searchLink><br />Lancet Gastroenterol Hepatol. 2025 Oct;10(10):896-903. (PMID: <searchLink fieldCode="PM" term="%2240816301%22">40816301)</searchLink><br />EClinicalMedicine. 2024 Dec 06;79:102968. (PMID: <searchLink fieldCode="PM" term="%2239720600%22">39720600)</searchLink><br />JMIR Med Educ. 2024 Jun 12;10:e58355. (PMID: <searchLink fieldCode="PM" term="%2238989834%22">38989834)</searchLink><br />JMIR Med Educ. 2024 Jul 18;10:e54793. (PMID: <searchLink fieldCode="PM" term="%2239023999%22">39023999)</searchLink><br />Radiol Artif Intell. 2022 Aug 17;4(5):e220074. (PMID: <searchLink fieldCode="PM" term="%2236204540%22">36204540)</searchLink><br />JMIR Med Inform. 2022 Nov 16;10(11):e37478. (PMID: <searchLink fieldCode="PM" term="%2236318697%22">36318697)</searchLink><br />PeerJ. 2024 Jul 19;12:e17793. (PMID: <searchLink fieldCode="PM" term="%2239040936%22">39040936)</searchLink><br />NPJ Digit Med. 2025 Mar 11;8(1):154. (PMID: <searchLink fieldCode="PM" term="%2240069303%22">40069303)</searchLink><br />JMIR Med Educ. 2024 Feb 20;10:e46500. (PMID: <searchLink fieldCode="PM" term="%2238376896%22">38376896)</searchLink><br />JMIR Med Educ. 2025 Apr 25;11:e66828. (PMID: <searchLink fieldCode="PM" term="%2240279148%22">40279148)</searchLink><br />Med Teach. 2024 Apr;46(4):446-470. (PMID: <searchLink fieldCode="PM" term="%2238423127%22">38423127)</searchLink><br />JMIR Med Educ. 2026 May 06;12:e89815. (PMID: <searchLink fieldCode="PM" term="%2242090298%22">42090298)</searchLink><br />Acad Med. 2023 Mar 1;98(3):348-356. (PMID: <searchLink fieldCode="PM" term="%2236731054%22">36731054)</searchLink><br />Soc Sci Med. 2022 Mar;296:114782. (PMID: <searchLink fieldCode="PM" term="%2235152047%22">35152047)</searchLink><br />Med Educ Online. 2025 Dec 31;30(1):2585637. (PMID: <searchLink fieldCode="PM" term="%2241327942%22">41327942)</searchLink><br />Biomed Eng Lett. 2025 Nov 1;15(6):1073-1083. (PMID: <searchLink fieldCode="PM" term="%2241280148%22">41280148)</searchLink><br />Br Med Bull. 2025 Jan 16;153(1):. (PMID: <searchLink fieldCode="PM" term="%2239821209%22">39821209)</searchLink><br />Med Teach. 2021 Oct;43(10):1139-1148. (PMID: <searchLink fieldCode="PM" term="%2234344274%22">34344274)</searchLink><br />Front Med (Lausanne). 2025 Jan 08;11:1522554. (PMID: <searchLink fieldCode="PM" term="%2239845830%22">39845830)</searchLink><br />Qual Health Res. 2016 Nov;26(13):1753-1760. (PMID: <searchLink fieldCode="PM" term="%2226613970%22">26613970)</searchLink><br />Cell Rep Med. 2023 Oct 17;4(10):101230. (PMID: <searchLink fieldCode="PM" term="%2237852174%22">37852174)</searchLink><br />Med Teach. 2021 Jul;43(7):758-764. (PMID: <searchLink fieldCode="PM" term="%2234061700%22">34061700)</searchLink>
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