ReviewJournal of clinical and translational research2021
Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.
Review in Journal of clinical and translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study.JMIR medical informatics · 2025Article
- Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.BMC oral health · 2024Article
- Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.Journal of dentistry (Shiraz, Iran) · 2023Review
- Candida Carriers among Individuals with Tongue Piercing-A Real-Time PCR Study.Antibiotics (Basel, Switzerland) · 2022Article
- Oral Fungal Infections: Past, Present, and Future.Frontiers in oral health · 2022Review
- Applications and Perspectives of Artificial Intelligence, Machine Learning and "Dentronics" in Dentistry: A Literature Review.Journal of International Society of Preventive & Community DentistryReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures.
aimThe aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice. RELEVANCE FOR PATIENTS: The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.
Indexed as
Identifiers
34541366PMC8445629What 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.