Exploring the integration of artificial intelligence into residency education curriculum: qualitative interviews

Abstract Introduction As artificial intelligence (AI) becomes more prevalent in medical practice, the importance of integrating AI curricula into postgraduate medical education (PGME) grows. While frameworks outlining necessary AI competencies for physicians exist, there is little consensus on how these competencies can be effectively implemented at the PGME level. Understanding the barriers and enablers to AI curricular integration is essential in preparing residents for an AI-enabled healthcare system. This study aims to explore expert perspectives on the ethical, institutional, and process-related challenges of integrating AI into PGME curriculum. Methods We conducted a qualitative study using an interpretive descriptive approach. Twelve physicians involved in AI and medical education across North America participated in semi-structured virtual interviews. Participants were recruited using purposive and snowball sampling. Interviews were audio-recorded and transcribed. Interview transcripts were analyzed using applied thematic analysis, with independent dual coding and consensus-based refinement of a structured codebook. Reflexive memos and analytic discussions supported rigor. Results Twelve interviews were completed; recruitment concluded at a pragmatic stopping point once successive interviews were judged to be converging on a consistent set of issues. Five major themes emerged: (1) the need for curricular agility in the face of rapid AI evolution, (2) gaps in faculty expertise, (3) limited institutional and regulatory readiness, (4) the role of nationally endorsed curricular standards, and (5) cultural resistance related to professional identity and responsibility. Participants emphasized prioritizing modular competencies, adopting co-learning models to address faculty limitations, and seeking clearer guidance from governing bodies. Conclusion Our findings offer practical guidance for educators on integrating AI into PGME curriculum. Building agile content, investing in multidisciplinary faculty development, fostering institutional and regulatory readiness, establishing national standards, and addressing cultural shifts can help PGME programs responsibly deploy AI curricula. In doing so, programs can position their graduates as reflective, ethical, and advanced clinicians capable of safely employing AI to improve patient care.

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Publication Details

Journal
BMC Medical Education
Published
2026-10-31
DOI
https://doi.org/10.1186/s12909-026-10684-y
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring the integration of artificial intelligence into residency education curriculum: qualitative interviews

Urmi Sheth, Brian Bursic, Matthew Sibbald, Sonaina Chopra et al.
8 links
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

Exploring the integration of artificial intelligence into residency education curriculum: qualitative interviews

Urmi Sheth, Brian Bursic, Matthew Sibbald, Sonaina Chopra, Leslie Martin
article en

Abstract

Abstract Introduction As artificial intelligence (AI) becomes more prevalent in medical practice, the importance of integrating AI curricula into postgraduate medical education (PGME) grows. While frameworks outlining necessary AI competencies for physicians exist, there is little consensus on how these competencies can be effectively implemented at the PGME level. Understanding the barriers and enablers to AI curricular integration is essential in preparing residents for an AI-enabled healthcare system. This study aims to explore expert perspectives on the ethical, institutional, and process-related challenges of integrating AI into PGME curriculum. Methods We conducted a qualitative study using an interpretive descriptive approach. Twelve physicians involved in AI and medical education across North America participated in semi-structured virtual interviews. Participants were recruited using purposive and snowball sampling. Interviews were audio-recorded and transcribed. Interview transcripts were analyzed using applied thematic analysis, with independent dual coding and consensus-based refinement of a structured codebook. Reflexive memos and analytic discussions supported rigor. Results Twelve interviews were completed; recruitment concluded at a pragmatic stopping point once successive interviews were judged to be converging on a consistent set of issues. Five major themes emerged: (1) the need for curricular agility in the face of rapid AI evolution, (2) gaps in faculty expertise, (3) limited institutional and regulatory readiness, (4) the role of nationally endorsed curricular standards, and (5) cultural resistance related to professional identity and responsibility. Participants emphasized prioritizing modular competencies, adopting co-learning models to address faculty limitations, and seeking clearer guidance from governing bodies. Conclusion Our findings offer practical guidance for educators on integrating AI into PGME curriculum. Building agile content, investing in multidisciplinary faculty development, fostering institutional and regulatory readiness, establishing national standards, and addressing cultural shifts can help PGME programs responsibly deploy AI curricula. In doing so, programs can position their graduates as reflective, ethical, and advanced clinicians capable of safely employing AI to improve patient care.

BMC Medical Education
McMaster University (CA)
Hamilton Health Sciences
Quality education, Good health and well-being
Openalex Percentile: Top 19%
Artificial Intelligence in Healthcare and Education
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