Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
John Mongan, Linda Moy, Charles E Kahn Jr
Radiol Artif Intell . 2020 Mar 25;2(2):e200029. doi: 10.1148/ryai.2020200029. eCollection 2020 Mar.
The advent of deep neural networks as a new artificial intelligence (AI) technique has engendered a large number of medical applications, particularly in medical imaging. Such applications of AI must remain grounded in the fundamental tenets of science and scientific publication (1). Scientific results must be reproducible, and a scientific publication must describe the authors’ work in sufficient detail to enable readers to determine the rigor, quality, and generalizability of the work, and potentially to reproduce the work’s results. A number of valuable manuscript checklists have come into widespread use, including the Standards for Reporting of Diagnostic Accuracy Studies (STARD) (2–5), Strengthening the Reporting of Observational studies in Epidemiology (STROBE) (6), and Consolidated Standards of Reporting Trials (CONSORT) (7,8). A radiomics quality score has been proposed to assess the quality of radiomics studies (9).
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