Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence

Abstract Temporomandibular disorders (TMDs) are a group of musculoskeletal and joint-related conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. They are among the most common causes of non-dental orofacial pain and functional impairment, significantly affecting quality of life. Despite advances in assessment and the development of standardized diagnostic systems such as the Research Diagnostic Criteria (RDC/TMD) and Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), accurate diagnosis remains difficult due to the multifactorial nature of TMDs, variability in symptoms, and subjectivity in pain reporting. Diagnostic accuracy is further limited by interexaminer variability, symptom overlap with other orofacial pain conditions, and restricted access to advanced imaging techniques. Artificial intelligence (AI) has emerged as a promising approach to address these challenges. Machine learning and deep learning algorithms can process complex imaging, clinical, and psychosocial data to improve diagnostic accuracy, consistency, and efficiency. AI-assisted imaging has shown strong performance in detecting disc displacement, degenerative changes, and other TMJ abnormalities, while predictive models based on symptoms, wearable sensors, and AI-driven decision-support tools are broadening diagnostic capabilities. This review summarizes current challenges in TMD diagnosis and highlights the growing role of AI in this field. Integrating AI technologies with established frameworks such as the DC/TMD may enable more objective, data-driven, and personalized diagnostic approaches. Ongoing interdisciplinary research, clinical validation, and ethical implementation are crucial for realizing AI's potential to transform TMD diagnosis and enhance patient outcomes.