AI-Driven Dental Caries Management Strategies: From Clinical Practice to Professional Education and Public Self Care.

Journal: International dental journal
Published Date: May 11, 2025

Abstract

Dental caries is one of the most prevalent chronic diseases among both children and adults, despite being largely preventable. This condition has significant negative impacts on human health and imposes a substantial economic burden. In recent years, scientists and dentists have increasingly started to utilize artificial intelligence (AI), particularly machine learning, to improve the efficiency of dental caries management. This study aims to provide an overview of the current knowledge about the AI-enabled approaches for dental caries management within the framework of personalized patient care. Generally, AI works as a promising tool that can be used by both dental professionals and patients. For dental professionals, it predicts the risk of dental caries by analyzing dental caries risk and protective factors, enabling to formulate personalized preventive measures. AI, especially those based on machine learning and deep learning, can also analyze images to detect signs of dental caries, assist in developing treatment plans, and help to make a risk assessment for pulp exposure during treatment. AI-powered tools can also be used to train dental students through simulations and virtual case studies, allowing them to practice and refine their clinical skills in a risk-free environment. Additionally, AI tracks brushing patterns and provides feedback to improve oral hygiene practices of the patients and the general population, thereby improving their understanding and compliance. This capability of AI can inform future research and the development of new strategies for dental caries management and control.

Authors

  • Yutong Liang
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Dongling Li
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Dongmei Deng
    Department of Preventive Dentistry, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Chun Hung Chu
    Faculty of Dentistry, University of Hong Kong, Hong Kong, China.
  • May Lei Mei
    Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand.
  • Yunpeng Li
    Centre for Oral, Clinical and Translational Sciences, Faculty of Dental, Oral and Craniofacial Sciences, King's College London, London, United Kingdom.
  • Na Yu
    National Dental Centre Singapore, Singapore.
  • Jinzhi He
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China. Electronic address: hejinzhi@scu.edu.cn.
  • Lei Cheng
    State Key Laboratory of Oral Diseases, Sichuan University, Chengdu, China.

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