The STARD-AI Reporting Guideline for Diagnostic Accuracy Studies Using Artificial Intelligence

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References

Cohen

Korevaar

Altman

, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799. doi:10.1136/bmjopen-2016-012799

Kashif Al-Ghita

Dawit

Kazi

, et al. Evaluation of imaging research adherence to the STARD 2015 reporting guideline: update 9 years after implementation and baseline assessment. Can Assoc Radiol J. 2025;76(4):631-645. doi:10.1177/08465371251324090

Gong

Soyer

McInnes

MDF

Patlas

MN.

Elements of a good radiology artificial intelligence paper. Can Assoc Radiol J. 2023;74(2):231-233. doi:10.1177/08465371221101195

Sounderajah

Guni

Liu

, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025;31(10):3283-3289. doi:10.1038/s41591-025-03953-8

Kashif Al-Ghita

Cobey

Moher

, et al. Cross-sectional evaluation of open science practices at imaging journals: a meta-research study. Can Assoc Radiol J. 2024;75(2):330-343. doi:10.1177/08465371231211290

Salameh

Moher

McGrath

, et al. Assessing adherence to the PRISMA-DTA guideline in diagnostic test accuracy systematic reviews: a five-year follow-up analysis. J Appl Lab Med. 2025;10(2):416-431. doi:10.1093/jalm/jfae117