Artificial Intelligence Education in Radiology Training: A Systematic Review of Effectiveness, Barriers, and Future Directions
92.9% of studies showed that AI-based training can enhance radiology trainees' knowledge, interpretive skills, or diagnostic performance, especially for junior trainees; however, its safe adoption requires standardized curricula with diverse cases, mentorship, workflow integration, and robust evaluation, with larger studies needed to confirm generalizability.
Authors
- Pedram Keshavarz (ORCID: https://orcid.org/0000-0001-5374-5514)
- Steven S. Raman (ORCID: https://orcid.org/0000-0003-0499-1676)
- Ryan Tai (ORCID: https://orcid.org/0000-0002-4309-6159)
- Arash Bedayat (ORCID: https://orcid.org/0000-0001-8769-0224)
- Zahra Mohammadigoldar
Institutions
- Trinity Health (US)
- University of California, Los Angeles (US)
- University of Massachusetts Chan Medical School (US)
Publication Details
- Journal
- Academic Radiology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.acra.2025.10.049
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00