Artificial intelligence (AI) has become increasingly integrated into dental diagnostics, imaging and treatment planning. However, despite this growing adoption, adherence to standardised reporting frameworks remains inconsistent. Insufficient use of established checklists continues to impede reproducibility, transparency and regulatory credibility. This review systematically examines existing AI reporting frameworks relevant to dental research, mapping their methodological domains, areas of overlap and persistent implementation gaps. We analysed established medical reporting guidelines for artificial intelligence in healthcare covering trials, protocols, prediction models, bias assessment, decision-support systems, imaging and systematic reviews alongside dentistry-specific checklists and ethical frameworks. While dental AI research is expanding rapidly, its reporting remains fragmented and inconsistent. Existing frameworks provide a comprehensive foundation for transparency and methodological rigour, but are underutilised. Harmonising these frameworks and promoting active adherence through journal policies, regulatory integration and quantitative compliance tracking are essential to bridge the gap between algorithmic performance and trustworthy clinical adoption.
Khurshid et al. (Thu,) studied this question.