医学和牙科教育中基于人工智能的问题和案例学习：系统回顾和元分析

IF 3.2 3区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

International dental journal Pub Date : 2025-06-26 DOI:10.1016/j.identj.2025.100858

Hongxia Wei , Yuguo Dai , Kaiting Yuan , Kar Yan Li , Kuo Feng Hung , Elaine Mingxin Hu , Angeline Hui Cheng Lee , Jeffrey Wen Wei Chang , Chengfei Zhang , Xin Li

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引用次数: 0

摘要

引言和目标人工智能（AI）技术的进步在医学和牙科教育领域引发了一场革命，这可能为应对传统的基于问题的学习（PBL）和基于案例的学习（CBL）的挑战提供有希望的解决方案。本研究的目的是评估人工智能驱动的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 Central Register of Controlled Trials和Web of Science上进行电子检索。纳入以英文发表并提供全文的临床试验，这些试验在医疗/牙科领域的PBL/CBL中实施了人工智能技术，并评估了知识获取、临床推理和/或满意度。质量评估由两名经过校准的评估员使用RoB 2进行。进行数据综合和meta分析，计算标准化平均差（SMD）或标准化平均差（SM）和95%置信区间（ci），并量化异质性。结果纳入了6个随机对照试验，总体偏倚风险被认为存在“一些担忧”。在知识获取方面，meta分析纳入了4项研究。检测到低异质性（I²= 20%），并采用固定效应模型。与对照组相比，人工智能干预显著提高了46%的知识获取（95% Cls [0.18-0.73], P = .001）。对于临床推理能力，由于研究方法和测量方法的异质性，统计分析是不可行的。选取三个研究进行学生满意度的元分析。异质性为中等（I²= 32%），选择通用逆方差法。合并SM评分为0.7 (95% Cls[0.47-0.92])，总体效果有统计学意义(P <；.00001)。尽管存在诸如纳入研究数量有限和总体偏见风险等局限性，但与传统学习方法相比，人工智能支持的PBL/CBL具有提高学生知识获取和学习者满意度的潜力。临床相关性不适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

AI-Powered Problem- and Case-based Learning in Medical and Dental Education: A Systematic Review and Meta-analysis

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.

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来源期刊

International dental journal 医学-牙科与口腔外科

CiteScore

4.80

自引率

6.10%

发文量

159

审稿时长

63 days

期刊介绍： The International Dental Journal features peer-reviewed, scientific articles relevant to international oral health issues, as well as practical, informative articles aimed at clinicians.