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

Journal: International dental journal
Published Date: Aug 1, 2025

Abstract

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.

Authors

  • Hongxia Wei
    Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, Guangdong, China.
  • Yuguo Dai
    School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
  • Kaiting Yuan
    Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Kar Yan Li
    Clinical Research Centre, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Kuo Feng Hung
    Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, SAR, China.
  • Elaine Mingxin Hu
    Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Angeline Hui Cheng Lee
    Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Jeffrey Wen Wei Chang
    Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Chengfei Zhang
    Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.

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