ArticleEuropean journal of dentistry2026
Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence.
Article in European journal of dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.