Evaluating dental AI research papers: Key considerations for editors and reviewers

Sergio E. Uribe, Manal H. Hamdan, Nicola Alberto Valente, Satoshi Yamaguchi, Fahad Umer, Antonin Tichy, Ruben Pauwels, Falk Schwendicke

Abstract: ObjectiveArtificial 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.MethodsEditors 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.ResultsThe 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.ConclusionEditors 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.

External IDs:doi:10.1016/j.jdent.2025.105867