Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.

ABSTRACT: Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluat

SUBMITTER: Moassefi M 

PROVIDER: S-EPMC10501962 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature