PUNE, India: Although the Diagnostic Criteria for Temporomandibular Disorders classification system has helped standardise temporomandibular disorder (TMD) assessment globally, its limitations and the complex presentation of TMD make diagnosis challenging. Artificial intelligence (AI) is being investigated as an adjunctive tool that could improve accuracy and consistency, and a recent systematic review has evaluated how well AI-based systems can identify temporomandibular joint (TMJ) abnormalities. The results point to AI’s potential, but also show that current evidence is preliminary and heterogeneous.
The review analysed five studies involving CBCT, MRI and panoramic radiography. The findings showed that AI-assisted tools achieved moderate to high diagnostic accuracy, although performance varied according to the diagnostic task, imaging modality and study design. Imaging-based assessment of osteoarthritis of the TMJ was most studied subtype in the review. In these studies, AI-assisted tools were able to detect early degenerative and morphological changes that might otherwise be overlooked during routine assessments.
One of the major advantages of AI-assisted diagnosis is consistency. Conventional TMD assessments can vary depending on the clinician’s experience and interpretation of imaging. AI systems can provide a more standardised form of image analysis, potentially helping to reduce subjectivity and improve diagnostic confidence. This could be especially valuable in busy dental clinics where rapid and accurate decision-making is essential.
The review reflected the wider trend of CBCT being studied in combination with AI technology. The researchers found that AI-enhanced CBCT analysis improved precision in identifying degenerative joint changes and condylar abnormalities. One study reported close agreement with expert clinicians on a diagnosis of TMJ osteoarthritis.
Despite these promising findings, the authors cautioned that the evidence is not strong enough to support clinical adoption of AI-assisted TMD diagnosis. Many of the studies involved small sample sizes and lacked external validation across diverse patient groups.
The review also emphasised that AI should be integrated into established TMD diagnostic frameworks. The authors noted that the Diagnostic Criteria for Temporomandibular Disorders protocol promotes a standardised approach that integrates clinical, psychosocial and imaging findings, in line with recent consensus guidance favouring patient-centred, conservative care. Integration of AI may help clinicians interpret complex information more consistently and support more personalised decision-making.
The authors recommended that future research should move beyond model accuracy and examine how AI tools affect real-world clinical decision-making. In particular, studies should assess whether explainable AI features, which make the basis for an AI system’s assessment more transparent to clinicians, improve diagnostic agreement and reduce over-reliance on automated outputs.
The article, titled “Comparing traditional versus AI-assisted TMJ disorder management approaches: A systematic review and meta-analysis”, was published online on 28 April 2026 in Clinical and Experimental Dental Research.
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