Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis
Year: 2026
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: OBJECTIVE: To systematically evaluate the effectiveness of AI-assisted teaching in computed tomography angiography (CTA) training, focusing on theoretical knowledge, practical skills, image judgment time, and teaching satisfaction. METHODS: Eight databases were searched for randomized controlled trials (RCTs) from 1990 to 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Cochrane RoB 2.0 tool. Frequentist and Bayesian meta-analyses were performed, along with subgroup analyses by CTA anatomical type, learner group, and sample size. RESULTS: Seven RCTs with 515 participants were included. All studies were conducted in China with Chinese-language AI interaction. Frequentist analyses showed that AI significantly improved practical skills and reduced image judgment time. Benefits were also observed for theoretical scores and teaching satisfaction. However, Bayesian analyses revealed statistical uncertainty for theoretical scores and teaching satisfaction due to high heterogeneity. Subgroup analyses identified CTA type, learner background, and sample size as key sources of heterogeneity. All included studies had high or some concerns for risk of bias. CONCLUSION: AI-assisted teaching effectively improves practical skills and shortens image judgment time in CTA training, offering clear educational benefits for radiology trainees. However, evidence for theoretical scores and teaching satisfaction remains uncertain due to high heterogeneity and methodological limitations. The findings support targeted integration of AI into CTA curricula while highlighting the need for large-scale, standardized RCTs to confirm long-term efficacy and generalizability.
Source: BMC Medical Education
Authors: Liang Yuan, Xingyi Xie, Yiqian Wen
Institution: Chongqing Medical University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7159689165