Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
University of California, San Francisco · New York University · +1 more institution
Abstract
T he advent of deep neural networks as a new artifi- cial 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…
Citation impact
- FWCI
- 65.53
- Percentile
- 100%
- References
- 34
Authors
3- JMJohn Mongan
University of California, San Francisco, New York University, University of Pennsylvania
- LMLinda Moy
University of California, San Francisco, New York University, University of Pennsylvania
- CECharles E. KahnCorresponding
University of California, San Francisco, New York University, University of Pennsylvania
Topics & keywords
- Checklist
- Generalizability theory
- Guideline
- Computer science
- Observational study
- Quality (philosophy)
- Medical physics
- Medical imaging