Methods: PubMed, SCOPUS, Science Direct, Web of Science, and Google Scholar electronic databases were searched systematically with restricting the languages to only English and year (January 2001 to March 2020), and studies were selected based on the inclusion criteria. Study quality and publication bias were assessed by using the Robvis, a software package of R statistical software.

Results: This systematic review included 32 studies (1172 patients) based on the inclusion and exclusion criteria. Most of the studies reported significant reduction of pain by the use of the laser during TMD treatment. Two-thirds of the study (78.13%) found a better outcome comparing with conventional one. According to Robvis, 84.4% of the studies were high methodological studies with low risk of bias.

Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.

Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.