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The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

  • Viknesh Sounderajah
  • , Ahmad Guni
  • , Xiaoxuan Liu
  • , Gary S. Collins
  • , Alan Karthikesalingam
  • , Sheraz R. Markar
  • , Robert M. Golub
  • , Alastair K. Denniston
  • , Shravya Shetty
  • , David Moher
  • , Patrick M. Bossuyt
  • , Ara Darzi
  • , Hutan Ashrafian*
  • , Suchi Saria
  • , Sherri Rose
  • , Patrick Bossuyt
  • , Leo Celi
  • , Karandeep Singh
  • , Johan Ordish
  • , Diana Samuel
  • David Moher, David Taylor, Glocker Ben Glocker, Trishan Panch, Sebastian Vollmer, Ravi Aggarwal, Penny Whiting, Pasha Normahani, Nenad Tomasev, Nader Rifai, Matthew D.F. McInnes, Lotty Hooft, Lena Maier-Hein, Leanne Harling, Karel Moons, Jonathan Pearson-Stuttard, Jonathan Godwin, Jérémie F. Cohen, Jeffrey De Fauw, Hugh Harvey, Felix Greaves, Dominic King, Darren Treanor, Daniel Ting, Christopher Kelly, Bilal A. Mateen, Amish Acharya, ,
*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The Standards for Reporting Diagnostic Accuracy (STARD) 2015 statement facilitates transparent and complete reporting of diagnostic test accuracy studies. However, there are unique considerations associated with artificial intelligence (AI)-centered diagnostic test studies. The STARD-AI statement, which was developed through a multistage, multistakeholder process, provides a minimum set of criteria that allows for comprehensive reporting of AI-centered diagnostic test accuracy studies. The process involved a literature review, a scoping survey of international experts, and a patient and public involvement and engagement initiative, culminating in a modified Delphi consensus process involving over 240 international stakeholders and a consensus meeting. The checklist was subsequently finalized by the Steering Committee and includes 18 new or modified items in addition to the STARD 2015 checklist items. Authors are encouraged to provide descriptions of dataset practices, the AI index test and how it was evaluated, as well as considerations of algorithmic bias and fairness. The STARD-AI statement supports comprehensive and transparent reporting in all AI-centered diagnostic accuracy studies, and it can help key stakeholders to evaluate the biases, applicability and generalizability of study findings.

Original languageEnglish
Pages (from-to)3283-3289
Number of pages7
JournalNature medicine
Volume31
Issue number10
DOIs
Publication statusPublished - Oct 2025

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