Artificial Intelligence Education in Radiology Training: A Systematic Review of Effectiveness, Barriers, and Future...

The purpose of this systematic review study was to characterize the current landscape of various artificial intelligence (AI) education in radiology, summarizing existing curricula, outcomes, challenges, and future directions for effective integration into residency training.

A comprehensive search of PubMed, Web of Science, Embase, and Google Scholar identified relevant published studies up to June 19, 2025.

Of the 2646 studies screened, 14 studies evaluated the performance of AI-based training programs for radiology trainees; among these, 92.9% (13/14) reported improvements in trainees' performance, including better diagnostic precision and interpretation (57.2%, 8/14), greater trainee confidence (57.2%, 8/14), hands-on experience with AI platforms (85.7%, 12/14), increased AI knowledge (85.7%, 12/14), engagement with AI-based case learning (35.7%, 5/14), understanding of AI ethics and bias (7.1%, 1/14), and acceptance of AI-assisted learning (78.6%, 11/14), whereas one study (7.1%, 1/14) found no significant benefit. Performance evaluation metrics varied across studies, with 35.7% (5/14) reporting a higher median of sensitivity, specificity, and accuracy (72%, 80%, and 81.3%) after AI training compared to before AI training (62.2%, 78.9%, and 76.5%, respectively), and 28.6% (4/14) showing improved AI knowledge scores. Hands-on simulations and didactic lectures were the most common AI training formats (78.6% and 71.4%). Risks and concerns included over-reliance on AI, limited exposure to complex or rare cases, and a lack of feedback. Recommendations highlighted the need for AI-faculty teaching, broader content coverage, and standardized multi-center AI-training programs to facilitate wider adoption.

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.