Evaluating dental AI research papers: Key considerations for editors and reviewers
Language English Country Great Britain, England Media print-electronic
Document type Journal Article
PubMed
40451605
DOI
10.1016/j.jdent.2025.105867
PII: S0300-5712(25)00311-2
Knihovny.cz E-resources
- Keywords
- Artificial intelligence, Deep learning, Dentistry, Machine learning, Peer-review,
- MeSH
- Humans MeSH
- Peer Review, Research * standards MeSH
- Peer Review MeSH
- Reproducibility of Results MeSH
- Dental Research * standards MeSH
- Artificial Intelligence * MeSH
- Research Design standards MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
OBJECTIVE: Artificial intelligence (AI) is increasingly used in dental research for diagnosis, treatment planning, and disease prediction. However, many dental AI studies lack methodological rigor, transparency, or reproducibility, and no dedicated peer-review guidance exists for this field. METHODS: Editors and reviewers from the ITU/WHO/WIPO AI for Health - Dentistry group participated in a structured survey and group discussions to identify key elements for reviewing AI dental research. A draft of the recommendations was circulated for feedback and consensus. RESULTS: The consensus from editors and reviewers identified four key indicators of high-quality AI dental research: (1) relevance to a real clinical or methodological problem, (2) robust and transparent methodology, (3) reproducibility through data/code availability or functional demos, and (4) adherence to ethical and responsible reporting practices. Common reasons for rejection included lack of novelty, poor methodology, limited external testing, and overstated claims. Four essential checks were proposed to support peer review: the study should address a meaningful clinical question, follow appropriate reporting guidelines (e.g., DENTAL-AI, STARD-AI), clearly describe reproducible methods, and use precise, justified, and clinically relevant wording. CONCLUSION: Editors and reviewers play a critical role in improving the quality of AI research in dentistry. This guidance aims to support more robust peer review and contribute to the development of reliable, clinically relevant, and ethically sound AI applications in dentistry.
Clinic for Conservative Dentistry and Periodontology LMU Klinikum Munich Germany
Department of Dentistry and Oral Health Aarhus University Aarhus Denmark
Department of Surgery Section Dentistry The Aga Khan University Hospital Karachi Pakistan
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