CLAIRE: a unified framework for reporting and assessing artificial intelligence in diagnostic imaging.
Authors
Affiliations (6)
Affiliations (6)
- Department of Dental Radiology and Imaging, Faculty of Dentistry, University of Fortaleza, 587 Dr. Valmir Pontes Avenue, Edson Queiroz, Fortaleza, CearĂ¡, 60812-020, Brazil.
- Department of Endodontics, Faculty of Dentistry, University of Fortaleza, 587 Dr. Valmir Pontes Avenue, Edson Queiroz, Fortaleza, CearĂ¡, 60812-020, Brazil.
- Faculty of Dentistry, University of Fortaleza, 587 Dr. Valmir Pontes Avenue, Edson Queiroz, Fortaleza, CearĂ¡, 60812-020, Brazil.
- Center for Technological Sciences, University of Fortaleza, 1321 Washington Soares Avenue, Room J01, Edson Queiroz, Fortaleza, CearĂ¡, 60812-020, Brazil.
- Department of Teleinformatics, Center of Technology, Federal University of CearĂ¡, Pici Campus - Block 725, Fortaleza, CearĂ¡, 60455-760, Brazil.
- Department of Stomatology, Faculty of Dentistry, University of Fortaleza, 587 Dr. Valmir Pontes Avenue, Edson Queiroz, Fortaleza, CearĂ¡, 60812-020, Brazil.
Abstract
Artificial intelligence (AI) models for diagnostic imaging face reproducibility challenges due to inconsistent reporting. Existing guidelines also lack specificity for imaging-based AI diagnostics, particularly regarding clinical usability and technical transparency. To address these gaps, the Completeness, Learnability, Applicability, Interpretability, Reproducibility, and Evaluation (CLAIRE) framework was developed as a practical reporting aid by a multidisciplinary team of clinicians and AI experts. CLAIRE was retrospectively validated on a subset of 10 imaging studies selected by theoretical saturation in medical and dental imaging. Internal validation demonstrated high reliability, with inter-rater agreement improving from Cohen's κ 0.286 to 0.987 (p < 0.01) after calibration, alongside a mean intra-rater reliability of 0.997 after a six-month washout period. This process yielded a 15-item structured checklist for standardising AI reporting, supported by an objective scoring system for quality categorisation and an editorial reference guide to facilitate systematic appraisal by reviewers and editors. CLAIRE aims to enhance clinician accessibility through plain-language technical summaries and assessments of real-world applicability. This proposal provides a unified and practical structure that improves reporting consistency, supports systematic assessment, and strengthens both reproducibility and clinical translation of AI-based imaging models.