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

Viknesh Sounderajah^1,2, Ahmad Guni^1,2, Xiaoxuan Liu^3,4, Gary S Collins⁵, Alan Karthikesalingam⁶, Sheraz R Markar^2,7, Robert M Golub⁸, Alastair K Denniston^3,4, Shravya Shetty⁹, David Moher¹⁰, Patrick M Bossuyt¹¹, Ara Darzi^1,2, Hutan Ashrafian^12,13,

¹Institute of Global Health Innovation, Imperial College London, London, UK.

²Department of Surgery and Cancer, Imperial College London, London, UK.

³Birmingham Health Partners Centre for Regulatory Science and Innovation, Birmingham, UK.

⁴College of Medicine and Health, University of Birmingham, Birmingham, UK.

⁵Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

⁶Google Research, London, UK.

⁷Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.

⁸Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

⁹Google Health, Palo Alto, CA, USA.

¹⁰Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.

¹¹Department of Epidemiology and Data Science, Amsterdam University Medical Centres, Duivendrecht, the Netherlands.

¹²Institute of Global Health Innovation, Imperial College London, London, UK. h.ashrafian@imperial.ac.uk.

¹³Department of Surgery and Cancer, Imperial College London, London, UK. h.ashrafian@imperial.ac.uk.

PubMed DOI

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 criteri…

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