Wei Hongxia, Dai Yuguo, Yuan Kaiting, Li Kar Yan, Hung Kuo Feng, Hu Elaine Mingxin, Lee Angeline Hui Cheng, Chang Jeffrey Wen Wei, Zhang Chengfei, Li Xin
Department of Stomatology, Liuzhou Workers' Hospital, Liuzhou Guangxi, P. R. China; Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Int Dent J. 2025 Aug;75(4):100858. doi: 10.1016/j.identj.2025.100858. Epub 2025 Jun 26.
INTRODUCTION AND AIMS: Advances in artificial intelligence (AI) technology have generated a revolution in medical and dental education, which may offer promising solutions to tackle the challenges of traditional problem-based learning (PBL) and case-based learning (CBL). The objective of this study was to assess the available evidence concerning AI-powered PBL/CBL on students' knowledge acquisition, clinical reasoning capability and satisfaction. METHODS: An electronic search was carried out on PubMed, MEDLINE, the Cochrane Central Register of Controlled Trials and Web of Science. Clinical trials published in English with full text available, which implemented AI technologies in PBL/CBL in the medical/dental field and evaluated knowledge acquisition, clinical reasoning and/or satisfaction were included. The quality assessment was conducted using RoB 2 by two calibrated assessors. Data synthesis and meta-analysis were performed, the standardised mean difference (SMD) or standardised mean (SM) and 95% confidence intervals (CIs) were calculated, and heterogeneity was quantified. RESULTS: Six randomized controlled trials were included, with an overall risk of bias judged to have 'some concerns'. For knowledge acquisition, 4 studies were included in the meta-analysis. A low heterogeneity (I² = 20%) was detected and a fixed-effect model was utilised. Compared with the control group, the AI intervention significantly improved knowledge acquisition by 46% (95% Cls [0.18-0.73], P = .001). For clinical reasoning capability, due to methodological and measurement heterogeneity among studies, statistical analysis was not feasible. Three studies were selected for the meta-analysis of students' satisfaction. Heterogeneity was moderate (I² = 32%), and a generic inverse variance method was selected. The pooled SM score was 0.7 (95% Cls [0.47-0.92]), and the overall effect was statistically significant (P < .00001). CONCLUSION: Despite limitations such as the limited number of included studies and the overall risk of bias concerns, AI-powered PBL/CBL has the potential to enhance students' knowledge acquisition and learner satisfaction compared to traditional learning approaches. CLINICAL RELEVANCE: Not applicable.
引言与目的：人工智能（AI）技术的进步在医学和牙科教育领域引发了一场革命，这可能为应对传统基于问题的学习（PBL）和基于案例的学习（CBL）所面临的挑战提供有前景的解决方案。本研究的目的是评估有关人工智能驱动的PBL/CBL对学生知识获取、临床推理能力和满意度的现有证据。 方法：在PubMed、MEDLINE、Cochrane对照试验中央注册库和科学网进行电子检索。纳入以英文发表的、全文可获取的临床试验，这些试验在医学/牙科领域的PBL/CBL中应用了人工智能技术，并评估了知识获取、临床推理和/或满意度。由两名经过校准的评估人员使用RoB 2进行质量评估。进行数据合成和荟萃分析，计算标准化平均差（SMD）或标准化均值（SM）以及95%置信区间（CIs），并对异质性进行量化。 结果：纳入了六项随机对照试验，总体偏倚风险被判定为“存在一些担忧”。对于知识获取，荟萃分析纳入了4项研究。检测到低异质性（I² = 20%），并采用了固定效应模型。与对照组相比，人工智能干预使知识获取显著提高了46%（95% Cls [0.18 - 0.73]，P = .001）。对于临床推理能力，由于研究之间方法和测量的异质性，统计分析不可行。选择了三项研究进行学生满意度的荟萃分析。异质性为中度（I² = 32%），选择了通用逆方差法。汇总的SM评分为0.7（95% Cls [0.47 - 0.92]），总体效应具有统计学意义（P < .00001）。 结论：尽管存在纳入研究数量有限和总体偏倚风险担忧等局限性，但与传统学习方法相比，人工智能驱动的PBL/CBL有潜力提高学生的知识获取和学习者满意度。 临床相关性：不适用。
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