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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • 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,
  • Amish Acharya,
  • Bilal A. Mateen,
  • Christopher Kelly,
  • Daniel Ting,
  • Darren Treanor,
  • Dominic King,
  • Felix Greaves,
  • Hugh Harvey,
  • Jeffrey De Fauw,
  • Jérémie F. Cohen,
  • Jonathan Godwin,
  • Jonathan Pearson-Stuttard,
  • Karel Moons,
  • Leanne Harling,
  • Lena Maier-Hein,
  • Lotty Hooft,
  • Matthew DF McInnes,
  • Melissa McCradden,
  • Nader Rifai,
  • Nenad Tomasev,
  • Pasha Normahani,
  • Penny Whiting,
  • Ravi Aggarwal,
  • Sebastian Vollmer,
  • Sheraz R. Markar,
  • Trishan Panch,
  • Ben Glocker,
  • David Taylor,
  • David Moher,
  • Diana Samuel,
  • Johan Ordish,
  • Karandeep Singh,
  • Leo Celi,
  • Patrick Bossuyt,
  • Sherri Rose,
  • Suchi Saria

摘要

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.