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Evaluating dental AI research papers: Key considerations for editors and reviewers
Sergio E Uribe1, Manal H Hamdan2, Nicola Alberto Valente3
1Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia & Institute of Stomatology, Riga Stradins University, Riga, Latvia; Clinic for Conservative Dentistry and Periodontology, LMU Klinikum, Munich, Germany.
Peer reviewers identified key indicators for high-quality artificial intelligence (AI) in dentistry research, focusing on relevance, methodology, reproducibility, and ethical reporting to enhance AI applications.
Area of Science:
- Dental research
- Artificial intelligence
- Health informatics
Background:
- Artificial intelligence (AI) is increasingly utilized in dental research for applications such as diagnosis, treatment planning, and disease prediction.
- A significant gap exists in methodological rigor, transparency, and reproducibility within current dental AI studies.
- There is a lack of dedicated peer-review guidelines specifically for artificial intelligence in dentistry.
Purpose of the Study:
- To establish key elements for the peer review of artificial intelligence (AI) research in dentistry.
- To develop consensus-based recommendations for improving the quality and reliability of AI applications in dental science.
- To provide guidance for editors and reviewers evaluating AI-driven dental research.
Main Methods:
- A structured survey and group discussions were conducted with editors and reviewers from the ITU/WHO/WIPO AI for Health - Dentistry group.
- Key elements for reviewing AI dental research were identified through expert consensus.
- A draft of recommendations was circulated for feedback and refinement to achieve consensus.
Main Results:
- Four critical indicators for high-quality AI dental research were identified: clinical relevance, robust/transparent methodology, reproducibility (data/code availability), and ethical reporting.
- Common reasons for rejection included lack of novelty, methodological flaws, insufficient external validation, and unsubstantiated claims.
- Four essential peer-review checks were proposed: meaningful clinical question, adherence to reporting guidelines (e.g., DENTAL-AI, STARD-AI), clear reproducible methods, and precise, justified language.
Conclusions:
- Editors and reviewers are crucial for enhancing the quality of AI research in dentistry.
- This guidance aims to foster more rigorous peer review processes for AI in dentistry.
- The recommendations support the development of dependable, clinically relevant, and ethically sound AI tools for dentistry.
