What question did this study set out to answer?

This review aims to assess how AI-based models perform in comparison to human experts for interpreting TMJ MRI.

April 19, 2026Open Access

Artificial Intelligence vs. Human Experts in Temporomandibular Joint MRI Interpretation: A Systematic Review

MLMarijus LeketasLithuanian University of Health Sciences ISInesa StonkutėLithuanian University of Health Sciences MMMiglė MiškinytėLithuanian University of Health Sciences

Key Points

This review aims to assess how AI-based models perform in comparison to human experts for interpreting TMJ MRI.
Conducted in line with PRISMA 2020 guidelines
Systematic search of multiple databases for relevant studies
Included studies analyzing AI models against expert interpretation
Evaluated metrics like sensitivity, specificity, and accuracy
Performed narrative synthesis due to heterogeneity.
Five studies included, with sample sizes from 118 to 1474 patients
AI models showed strong discriminative performance with AUC values from 0.79 to 0.98
AI had diagnostic accuracy comparable to experienced radiologists
AI showed higher specificity but human experts had higher sensitivity
In osteoarthritis evaluation, AI performed at expert level.

Abstract

Background: Magnetic resonance imaging (MRI) is the reference standard for evaluating temporomandibular joint (TMJ) disorders, particularly for assessing disc position, joint effusion, and degenerative changes. With increasing imaging demands and advances in deep learning, artificial intelligence (AI) has emerged as a potential adjunct to expert interpretation. This systematic review aimed to compare the diagnostic performance of AI-based models with that of human experts in TMJ MRI analysis. Methods: This review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251174127). A systematic search of PubMed/MEDLINE, ScienceDirect, Wiley Online Library, and Springer Nature Link was performed for studies published between 2020 and 2026. Eligible studies included human participants undergoing TMJ MRI and evaluated AI, machine learning, or deep learning models against human expert interpretation. Extracted outcomes included sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and agreement metrics. Risk of bias was assessed using QUADAS-2. Due to substantial heterogeneity, a narrative synthesis was conducted. Results: Five retrospective diagnostic accuracy studies were included, comprising sample sizes ranging from 118 to 1474 patients. Target conditions included anterior disc displacement, joint effusion, osteoarthritis, and disc perforation. AI models demonstrated strong discriminative performance, with reported AUC values ranging from 0.79 to 0.98. In direct comparisons, AI achieved diagnostic accuracy comparable to experienced radiologists. AI systems frequently demonstrated higher specificity and similar overall accuracy, whereas human experts often showed higher sensitivity. In osteoarthritis assessment, AI performance approached expert level and exceeded that of less experienced readers. All studies were retrospective and predominantly single-center, with heterogeneous reference standards and limited external validation. Conclusions: AI achieves diagnostic performance comparable to experienced clinicians in TMJ MRI interpretation and shows promise as a decision-support tool. Nevertheless, it should be regarded as complementary to, rather than a replacement for, expert radiological assessment pending further rigorous validation.

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