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Peer-reviewedCC BYnpj Digital Medicine

AI-PACE: a framework for integrating AI into medical education

  • Scott McGrath1
  • Katherine K. Kim2
  • Karnjit Johl3
  • Haibo Wang4
  • Nick Anderson2
4 affiliations
  1. 1Center for Information Technology in the Interest of Society, University of California Berkeley, Berkeley, CA, USA. [email protected]
  2. 2School of Medicine, Department of Public Health Sciences, University of California Davis, Davis, CA, USA
  3. 3School of Medicine, Department of Internal Medicine, University of California Davis, Davis, CA, USA
  4. 4Research Centre of Big Data and AI for Medicine First Affiliated Hospital of Sun Yan-Sen University Guangzhou, Guangzhou, China
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Abstract

Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016-2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom's Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.

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Cite

BibTeXRIS
BibTeX
@article{mcgrath2026aipace,
  title = {{AI-PACE: a framework for integrating AI into medical education}},
  author = {Scott McGrath and Katherine K. Kim and Karnjit Johl and Haibo Wang and Nick Anderson},
  journal = {npj Digital Medicine},
  year = {2026},
  month = may,
  doi = {10.1038/s41746-026-02768-2},
  url = {https://doi.org/10.1038/s41746-026-02768-2},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AI-PACE: a framework for integrating AI into medical education
AU  - Scott McGrath
AU  - Katherine K. Kim
AU  - Karnjit Johl
AU  - Haibo Wang
AU  - Nick Anderson
PY  - 2026
DA  - 2026/05/16
JO  - npj Digital Medicine
DO  - 10.1038/s41746-026-02768-2
UR  - https://doi.org/10.1038/s41746-026-02768-2
AB  - Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016-2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom's Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.
SN  - 2398-6352
ER  -