Effectiveness of AI-assisted medical education for Chinese undergraduate medical students: a meta-analysis

Abstract

In recent years, artificial intelligence (AI) has been adopted as an innovative teaching method. However, no studies have comprehensively evaluated the effectiveness of AI in the context of medical education in China. This study aimed to assess the effectiveness of AI-assisted teaching methods versus traditional teaching methods regarding their impact on medical education outcomes in China. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and retrieved studies published in the Web of Science, PubMed, EMBASE, the Cochrane Library, the Chinese National Knowledge Infrastructure, VIP Database and the Chinese Wanfang Database from January 2015 to December 2024. The standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated, and heterogeneity was evaluated using I² statistics, with subsequent meta-regression analysis employed to identify the contributing factors. Twelve studies involving 824 medical students were included. All studies provided usable data on examination scores. The pooled analysis revealed a significant difference in favor of AI-assisted teaching compared with traditional teaching methods (SMD = 2.06, 95% CI: 1.35–2.76). In addition, AI-assisted teaching significantly increased student satisfaction in eight studies (OR = 5.80, 95% CI: 3.30–10.18). Meta-regression analysis indicated that randomization, study duration and type of AI technologies were the primary factors contributing to heterogeneity. The AI-assisted approach to medical education in China was shown to be more effective than traditional teaching methods in improving examination scores and student satisfaction, offering substantial evidence for the adoption of AI in medical education. These results highlighted the potential advantages of incorporating AI into medical teaching practices. Future research should focus on investigating the effectiveness of AI-assisted teaching across diverse educational systems and applications.

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