A bibliometric analysis of clinical research trends in temporomandibular disorders and associated pain: from pain management to AI-assisted diagnosis and treatment

Background: This study aimed to provide an analysis of the global research status, collaboration networks, core hotspots, and emerging trends in temporomandibular disorders (TMD) and associated pain using bibliometric methods, to identify research gaps and provide references for future basic and translational research. Methods: Publications on temporomandibular disorders and associated pain published between 01 January 2016 and 01 April 2026 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer were used to analyse countries/regions, authors, journals, institutions, funding agencies, and keywords. Results: A total of 4917 publications were identified. The annual number of publications showed a steady increasing trend. The United States was the leading country in global research in this field, with 904 publications, followed by Brazil (697) and China (650). The Universidade de São Paulo was the most productive institution (233). The Journal of Oral Rehabilitation ranked first among journals with 519 publications. Keyword analysis revealed that keywords with the highest burst strength in recent years included “artificial intelligence” (7.75), “pain management” (7.23), “etiology” (5.97), and “oral behaviours” (5.05), indicating potential future research directions. Conclusions: Research in this field has shown a steady upward trend. Current hotspots focus on four major themes:(1) organic pathological changes and diagnosis of the temporomandibular joint; (2) pain assessment and comorbidity research; (3) epidemiological characteristics, risk factors, and psychological comorbidities; and (4) clinical management, rehabilitation strategies, and evidence-based interventions. This study also suggests that standardised pain management and Artificial Intelligence (AI)-assisted diagnosis and treatment are emerging and promising directions.

Temporomandibular disorders; Pain; Research trends; Bibliometric analysis; Artificial intelligence

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