Artificial Intelligence Reporting Guidelines in Dentomaxillofacial Radiology: Specialty-Oriented Practical Guide.
Authors
Affiliations (4)
Affiliations (4)
- Postgraduate Program in Dentistry, Dentomaxillofacial Radiology and Imaging Laboratory, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil.
- Department of Diagnosis and Surgery, The Institute of Sciences and Technology, São Paulo State University (UNESP), São José dos Campos 12245-000, SP, Brazil.
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey.
- Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas 13083-887, SP, Brazil.
Abstract
Artificial intelligence (AI) is becoming an increasingly important component of dentomaxillofacial radiology, with applications ranging from panoramic radiography and cone-beam computed tomography (CBCT) to magnetic resonance imaging, radiomics, and other emerging AI approaches. As AI studies become more methodologically complex, concerns regarding transparency, reproducibility, validation, and clinical applicability have also increased. Several reporting resources, including reporting guidelines, methodological quality assessment tools, and broader principles for trustworthy AI, have been developed to improve the quality and transparency of AI research. However, selecting the most appropriate resource remains challenging, particularly for studies that combine radiomics, predictive modeling, diagnostic accuracy assessment, segmentation, multimodal AI, and clinical validation. This specialty-oriented practical guide provides an overview of the main AI reporting resources and offers practical recommendations to support their selection and complementary use in dentomaxillofacial radiology. Common study designs, methodological challenges, and reporting considerations are discussed, together with practical guidance for readers and reviewers. The complementary roles of different reporting resources are illustrated through tables, decision aids, conceptual figures, and examples from the dentomaxillofacial imaging literature. By promoting more transparent and consistent reporting practices, this guide aims to support the development of more reproducible, clinically relevant, and trustworthy AI research in dentomaxillofacial radiology.