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

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

doi:10.1038/s41591-025-03953-8

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

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…