Related Experiment Video
Updated: Sep 14, 2025
05:49
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
999
Machine learning in dentistry: a scoping review
Shrey Lakhotia1, Hormazd Godrej2, Amandeep Kaur3
1Helios Enter Data Warehouse IT Exp., Henry Ford Health System, Detroit, Michigan, United States of America.
PLOS Digital Health
|July 23, 2025
Summary
Machine learning (ML) in dentistry shows promise for diagnosis and treatment, but many studies lack methodological rigor. Improving model validation, bias assessment, and reproducibility is crucial for real-world AI adoption in dental care.
Area of Science:
- Dental informatics
- Machine learning applications
- Medical AI
Background:
- Artificial intelligence (AI), particularly machine learning (ML), is increasingly utilized in dental applications for diagnosis, prognosis, and treatment planning.
- Despite widespread adoption, a comprehensive assessment of the methodological quality and reporting completeness of these ML models in dentistry is lacking.
Purpose of the Study:
- To conduct a scoping review of published literature on ML in dentistry.
- To evaluate the methodological completeness and reporting quality of ML models using the TRIPOD + AI rubric.
- To identify key gaps and areas for improvement in ML research for dental applications.
Main Methods:
- A scoping review of PubMed-indexed articles published between January 1, 2018, and December 31, 2023, using ML in any dental specialty.
- Studies were assessed using the TRIPOD + AI checklist, focusing on data preprocessing, model validation, and clinical performance reporting.
- 1,506 articles were identified, with 280 meeting the inclusion criteria for detailed analysis.
Main Results:
- Oral and maxillofacial radiology, surgery, and general dentistry were the most represented specialties.
- A significant proportion of studies (22.9%) failed to compare their models against a clinical reference standard or existing model.
- Common limitations included insufficient bias assessment, poor outlier reporting, inadequate calibration evaluation, low reproducibility, and restricted data access.
Conclusions:
- While ML holds transformative potential for dental care, critical improvements in model calibration assessment and equity evaluation are necessary for successful clinical implementation.
- Future research should prioritize enhancing error explainability, outlier reporting, reproducibility, fairness assessments, and conducting prospective validation studies.
