ArticleJournal of clinical medicine2023
Machine Learning in Dentistry: A Scoping Review.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 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
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes.BMC medical education · 2026Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Knowledge and Awareness of Performance Metrics in Dental Image Analysis With Artificial Intelligence: A Cross-Sectional Survey of Researchers and Clinicians.International dental journal · 2026Article
- From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial Intelligence in Dentistry-A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Emerging Artificial Intelligence Technologies for Evaluation of Dental Composite Restorations: A Scoping Review.European journal of dentistry · 2026Article
- Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN): Can large language models match the performance of orthodontists?Korean journal of orthodontics · 2026Article
- Rethinking Diagnostic Performance Metrics in Imaging Artificial Intelligence: Lessons from Caries Detection on Radiographs.Caries research · 2026Review
- Restoration's Longevity in Endodontically Treated Teeth: A Machine Learning Survival Analysis From Randomised Clinical Trials.International endodontic journal · 2026Article
- Artificial Intelligence in Dentistry: A Concise Review of Reporting Checklists and Guidelines.International dental journal · 2026Review
- Deep learning for Angle classification based on intraoral photographs: an interpretability perspective.BMC oral health · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective.Cureus · 2025Review
- The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.Health science reports · 2025Article
- Machine learning-assisted prediction of clinical responses to periodontal treatment.Journal of periodontology · 2025Article
- Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning in dentistry: a scoping review.PLOS digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
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.