Meta-analysis of randomized controlled trials comparing outcomes of artificial intelligence-based teaching versus traditional-based teaching in medical education.

AIMS: To evaluate comparative outcomes of artificial Intelligence (AI)-based and traditional-based teaching in medical education. METHODS: The literature search was carried out in CENTRAL, CINAHL, Web of Science, MEDLINE, and EMBASE to identify randomized controlled trials (RCTs) comparing AI-based versus traditional-based teachings in medical education. Estimate of effect size was determined for knowledge score, skills score, and teaching satisfaction score via fixed-effect modelling. RESULTS: Fourteen RCTs enrolling 1116 students who received AI-based teaching (n = 558) or traditional-based teaching (n = 558) were included. The use of AI-based teaching was associated with significantly higher knowledge score (standardized mean difference (SMD): 0.36, 95% CI, 0.24-0.49, P < .00001), skills score (SMD: 0.78, 95% CI, 0.57-0.99, P < .00001), and teaching satisfaction score (SMD: 0.97, 95% CI, 0.66-1.29, P < .00001) compared to the traditional-based teaching. Subgroup analyses with respect to the practical course, theoretical course, duration of course shorter or longer than 1 week were consistent with the main analyses. Meta-regression analysis demonstrated that practical course significantly increased estimate effect for knowledge score (P = .002) and skills score (P = .0001). CONCLUSIONS: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. However, the available evidence may be subject to publication and reporting bias with high between-study heterogeneity. Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings. Key messages What is already known on this topic: Growing evidence from randomized controlled trials demonstrated positive impact of artificial intelligence (AI) in medical education when compared to the traditional approaches. What this study adds: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. How this study might affect research, practice, or policy: This study suggests that Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings.