« Geri
META-RESEARCH ON REPORTING GUIDELINES FOR ARTIFICIAL INTELLIGENCE: ARE AUTHORS AND REVIEWERS ENCOURAGED ENOUGH IN RADIOLOGY, NUCLEAR MEDICINE, AND MEDICAL IMAGING JOURNALS?
BURAK KOAK AL KELE FADME KSE
Diagnostic and Interventional Radiology - 2024;30(5):291-298
University of Health Sciences, Baakehir am and Sakura City Hospital, Clinic of Radiology, stanbul, Trkiye

PURPOSE To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. METHODS The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first-and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. RESULTS Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). CONCLUSION The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful.

Facebook'ta Payla