AI-PACE: A Framework for Integrating AI into Medical Education
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910249329360896 |
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| author | McGrath, Scott P. Kim, Katherine K. Johl, Karnjit Wang, Haibo Anderson, Nick |
| author_facet | McGrath, Scott P. Kim, Katherine K. Johl, Karnjit Wang, Haibo Anderson, Nick |
| contents | The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10527 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-PACE: A Framework for Integrating AI into Medical Education McGrath, Scott P. Kim, Katherine K. Johl, Karnjit Wang, Haibo Anderson, Nick Computers and Society Artificial Intelligence K.3.2; J.3; I.2.1 The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment. |
| title | AI-PACE: A Framework for Integrating AI into Medical Education |
| topic | Computers and Society Artificial Intelligence K.3.2; J.3; I.2.1 |
| url | https://arxiv.org/abs/2602.10527 |