Khattak Osama, Hashem Ahmed Shawkat, Alqarni Mohammed Saad, Almufarrij Raha Ahmed Shamikh, Siddiqui Amna Yusuf, Anis Rabia, Ahmad Shahzad, Fareed Muhammad Amber, Alothmani Osama Shujaa, Alkhershawy Lama Habis Samah, Alabidin Wesam Waleed Zain, Issrani Rakhi, Agarwal Anshoo
Department of Restorative Dentistry, College of Dentistry, Jouf University, Sakaka 72311, Saudi Arabia.
Oral Medicine and Periodontology, Faculty of Dentistry, Damanhour University, Damanhur 22522, Egypt.
Healthcare (Basel). 2025 Jun 18;13(12):1466. doi: 10.3390/healthcare13121466.
: AI has been adopted in dentistry for diagnosis, decision making, and therapy prognosis prediction. This systematic review aimed to identify AI models in dentistry, assess their performance, identify their shortcomings, and discuss their potential for adoption and integration in dental practice in the future. : The sources of the papers were the following electronic databases: PubMed, Scopus, and Cochrane Library. A total of 20 out of 947 needed further studies, and this was encompassed in the present meta-analysis. It identified diagnostic accuracy, predictive performance, and potential biases. : AI models demonstrated an overall diagnostic accuracy of 82%, primarily leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs). These models have significantly improved the diagnostic precision for dental caries compared with traditional methods. Moreover, they have shown potential in detecting and managing conditions such as bone loss, malignant lesions, vertical root fractures, apical lesions, salivary gland disorders, and maxillofacial cysts, as well as in performing orthodontic assessments. However, the integration of AI systems into dentistry poses challenges, including potential data biases, cost implications, technical requirements, and ethical concerns such as patient data security and informed consent. AI models may also underperform when faced with limited or skewed datasets, thus underscoring the importance of robust training and validation procedures. : AI has the potential to revolutionize dentistry by significantly improving diagnostic accuracy and treatment planning. However, before integrating this tool into clinical practice, a critical assessment of its advantages, disadvantages, and utility or ethical issues must be established. Future studies should aim to eradicate existing barriers and enhance the model's ease of understanding and challenges regarding expense and data protection, to ensure the effective utilization of AI in dental healthcare.
人工智能已被应用于牙科领域，用于诊断、决策制定和治疗预后预测。本系统评价旨在识别牙科领域的人工智能模型，评估其性能，找出其缺点，并探讨其未来在牙科实践中的应用和整合潜力。论文来源为以下电子数据库：PubMed、Scopus和Cochrane图书馆。947篇论文中有20篇需要进一步研究，这些被纳入了本荟萃分析。分析确定了诊断准确性、预测性能和潜在偏差。人工智能模型的总体诊断准确率为82%，主要利用人工神经网络（ANN）和卷积神经网络（CNN）。与传统方法相比，这些模型显著提高了龋齿的诊断精度。此外，它们在检测和管理诸如骨质流失、恶性病变、垂直根折、根尖病变、唾液腺疾病和颌面部囊肿等病症以及进行正畸评估方面也显示出潜力。然而，将人工智能系统整合到牙科领域存在挑战，包括潜在的数据偏差、成本问题、技术要求以及诸如患者数据安全和知情同意等伦理问题。当面对有限或有偏差的数据集时，人工智能模型的表现可能也会不佳，因此凸显了强大的训练和验证程序的重要性。人工智能有潜力通过显著提高诊断准确性和治疗计划来彻底改变牙科领域。然而，在将此工具整合到临床实践之前，必须对其优点、缺点、实用性或伦理问题进行严格评估。未来的研究应旨在消除现有障碍，提高模型的易理解性，并应对费用和数据保护方面的挑战，以确保人工智能在牙科医疗保健中的有效利用。
