Artificial intelligence in dental research: Checklist for authors, reviewers, readers

Objectives: The number of studies employing artificial intelligence (AI), specifically machine and deep learning, is growing fast. The majority of studies suffer from limitations in planning, conduct and reporting, resulting in low robustness, reproducibility and applicability. We here present a consented checklist on planning, conducting and reporting of AI studies for authors, reviewers and readers in dental research. Methods: Lending from existing reviews, standards and other guidance documents, an initial draft of the checklist and an explanatory document were derived and discussed among the members of IADR's e-oral network and the ITU/WHO focus group Artificial Intelligence for Health (AI4H). The checklist was consented by 27 group members via an e-Delphi process. Results: Thirty-one items on planning, conducting and reporting of AI studies were agreed on. These involve items on the studies' wider goal, focus, design and specific aims, data sampling and reporting, sample estimation, reference test construction, model parameters, training and evaluation, uncertainty and explainability, performance metrics and data partitions. Conclusion: Authors, reviewers and readers should consider this checklist when planning, conducting, reporting and evaluating studies on AI in dentistry. Clinical significance: Current studies on AI in dentistry show considerable weaknesses, hampering their replication and application. This checklist may help to overcome this issue and advance AI research as well as facilitate a debate on standards in this fields.

Automated summary

The study highlights the rapid growth of artificial intelligence (AI) studies in dentistry, with many facing limitations in planning, conduct, and reporting. A checklist was developed with input from experts in the field and agreed upon by 27 group members, focusing on various aspects of AI research in dentistry including study goals, design, data handling, and more. The checklist aims to improve the robustness, reproducibility, and applicability of AI studies in dentistry.