Artificial Intelligence in Diagnosing and Treating Temporomandibular Joint Disorders: A Review

<ns3:p>Introduction With the rapid advancement in artificial intelligence (AI), its integration into medical fields has gained momentum. AI is increasingly applied for diagnosing and managing temporomandibular joint disorders (TMD), affecting one of the most complex joints in the body. This review aims to highlight the AI-driven approaches in diagnosing and treating TMD. Methods A comprehensive literature search conducted using PubMed, Web of Science, and Google Scholar, focusing on English-language studies published since 2007. References from relevant articles were also examined. Results A total of 21 studies met the inclusion criteria, covering diverse AI applications in TMD. These studies included AI-driven medical image segmentation (3), juvenile idiopathic arthritis (3), temporomandibular joint osteoarthritis (TMJOA) detection (4), Temporomandibular joint (TMJ) articular disc displacements and deformities (5), decision support systems (5), and AI-based sound analysis for TMJ assessment (1). The findings suggest that AI, particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated promising accuracy in detecting and classifying TMD and its related pathologies. AI automates segmentation, improves diagnostic consistency, and assists clinicians through predictive modeling. Discussion Despite advancements, challenges remain, as many studies rely on small datasets, limiting generalizability. The absence of standardized evaluation metrics and external validation hinders clinical adoption. Ethical concerns, data privacy issues, and workflow integration pose additional barriers. Future research should focus on larger datasets, model interpretability, and rigorous clinical validation. Conclusion AI is emerging as a valuable tool in TMD diagnosis and management, promising improved accuracy and efficiency, but further development is essential for real-world implementation.</ns3:p>