SynthesisPain research & management2021
Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.
Synthesis in Pain research & management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Application, performance and limitations of artificial intelligence for the diagnosis and prediction of oro-facial pain: a systematic review.Frontiers in oral health · 2026Pooled it
- The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.Journal of oral & facial pain and headache · 2026Review
- Salivary Oxidative Stress Biomarkers in Temporomandibular Disorders: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2026Review
- AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns.Scientific reports · 2026Article
- [Formula: see text] : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness.BMC oral health · 2025Article
- A machine learning-based risk prediction model for diabetic oral ulceration.BMC oral health · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- Integration of Artificial Intelligence in Dentistry: A Systematic Review of Educational and Clinical Implications.Cureus · 2025Review
- Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review.International journal of oral science · 2023Review
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model.International journal of environmental research and public health · 2023Article
- Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation.Scientific reports · 2023Article
- Artificial intelligence in dentistry-A review.Frontiers in dental medicine · 2023Review
- Article
- Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis.Journal of clinical periodontology · 2022Article
- Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29 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. Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.