Feher Balazs, Tussie Camila, Giannobile William V
Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, United States.
ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland.
Front Artif Intell. 2024 Jul 23;7:1427517. doi: 10.3389/frai.2024.1427517. eCollection 2024.
Artificial intelligence (AI) is increasingly applied across all disciplines of medicine, including dentistry. Oral health research is experiencing a rapidly increasing use of machine learning (ML), the branch of AI that identifies inherent patterns in data similarly to how humans learn. In contemporary clinical dentistry, ML supports computer-aided diagnostics, risk stratification, individual risk prediction, and decision support to ultimately improve clinical oral health care efficiency, outcomes, and reduce disparities. Further, ML is progressively used in dental and oral health research, from basic and translational science to clinical investigations. With an ML perspective, this review provides a comprehensive overview of how dental medicine leverages AI for diagnostic, prognostic, and generative tasks. The spectrum of available data modalities in dentistry and their compatibility with various methods of applied AI are presented. Finally, current challenges and limitations as well as future possibilities and considerations for AI application in dental medicine are summarized.
人工智能(AI)正越来越多地应用于医学的各个学科,包括牙科。口腔健康研究中机器学习(ML)的应用正在迅速增加,ML是AI的一个分支,它以类似于人类学习的方式识别数据中的内在模式。在当代临床牙科中,ML支持计算机辅助诊断、风险分层、个体风险预测和决策支持,以最终提高临床口腔保健效率、改善治疗效果并减少差异。此外,从基础和转化科学到临床研究,ML在牙科和口腔健康研究中的应用也越来越广泛。从ML的角度来看,本综述全面概述了牙科如何利用AI进行诊断、预后和生成任务。介绍了牙科中可用的数据模式及其与各种应用AI方法的兼容性。最后,总结了AI在牙科医学应用中的当前挑战和局限性以及未来的可能性和考虑因素。