Interactive reporting checklist for diagnostic accuracy studies that evaluate artificial intelligence and machine learning index tests.
About This Tool: The STARD-AI Checklist
What is it for?
This tool is a web-based checklist implementing the STARD-AI (Standards for Reporting Diagnostic Accuracy – Artificial Intelligence) reporting guideline.
Its primary purpose is to serve as a reporting completeness checklist for diagnostic test accuracy studies that evaluate an AI- or machine-learning-based index test. STARD-AI is a single integrated 40-item reporting guideline for AI diagnostic accuracy studies. It incorporates inherited STARD 2015 items, modifies selected STARD 2015 items, and introduces additional AI-specific items. By using this checklist, authors, editors and reviewers can systematically evaluate whether a study transparently reports the elements needed to appraise the bias, applicability and generalisability of an AI diagnostic test.
This web tool is provided for educational and review purposes only and does not constitute formal editorial, regulatory, methodological, clinical, or legal advice. Please consult the original publications for the full official guidance.
Sources and attribution
Checklist items are derived from the STARD-AI reporting guideline. Explanatory summaries have been independently rewritten for educational and review purposes, drawing on the STARD-AI guideline and the STARD 2015 Explanation & Elaboration document for inherited diagnostic accuracy concepts.
1. Sounderajah, V., Guni, A., Liu, X. et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.
Nat Med 31, 3283–3289 (2025).
doi:10.1038/s41591-025-03953-8.
2. Cohen, J.F., Korevaar, D.A., Altman, D.G. et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration.
BMJ Open 6, e012799 (2016).
doi:10.1136/bmjopen-2016-012799.
This checklist is not endorsed by the original authors.