{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Information Technology in the Interest of Society, University of California Berkeley, Berkeley, CA, USA. smcgrath@berkeley.edu",
    "School of Medicine, Department of Public Health Sciences, University of California Davis, Davis, CA, USA",
    "School of Medicine, Department of Internal Medicine, University of California Davis, Davis, CA, USA",
    "Research Centre of Big Data and AI for Medicine First Affiliated Hospital of Sun Yan-Sen University Guangzhou, Guangzhou, China"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Scott McGrath",
      "orcid": "0000-0002-0065-726X"
    },
    {
      "affs": [
        1
      ],
      "name": "Katherine K. Kim"
    },
    {
      "affs": [
        2
      ],
      "name": "Karnjit Johl",
      "orcid": "0000-0002-2004-7223"
    },
    {
      "affs": [
        3
      ],
      "name": "Haibo Wang",
      "orcid": "0000-0003-0818-138X"
    },
    {
      "affs": [
        1
      ],
      "name": "Nick Anderson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/28sq1px8zv90x.bib",
  "cited_by": 16,
  "doi": "10.1038/s41746-026-02768-2",
  "fulltext_url": null,
  "id": "28sq1px8zv90x",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02768-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
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  "openalex": "W7161317119",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02768-2_reference.pdf",
  "pmcid": "PMC13434227",
  "pmid": "42141085",
  "published": "2026-05-16",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/28sq1px8zv90x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:56Z",
      "native_id": "W7161317119",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41746-026-02768-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "AI-PACE: a framework for integrating AI into medical education",
  "title_text": "AI-PACE: a framework for integrating AI into medical education",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:16Z",
  "url": "https://research.draconitic.com/paper/28sq1px8zv90x",
  "venue": "npj Digital Medicine",
  "versions": []
}