Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification Journal Article
| Authors: | Kline, T. L.; Kitamura, F.; Warren, D.; Pan, I.; Korchi, A. M.; Tenenholtz, N.; Moy, L.; Gichoya, J. W.; Santos, I.; Moradi, K.; Avval, A. H.; Alkhulaifat, D.; Blumer, S. L.; Hwang, M. Y.; Git, K. A.; Shroff, A.; Stember, J.; Walach, E.; Shih, G.; Langer, S. G. |
| Article Title: | Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification |
| Abstract: | Recent advances in Artificial Intelligence (AI) methodologies and their application to medical imaging has led to an explosion of related research programs utilizing AI to produce state-of-the-art classification performance. Ideally, research culminates in dissemination of the findings in peer-reviewed journals. To date, acceptance or rejection criteria are often subjective; however, reproducible science requires reproducible review. The Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine (SIIM) has identified a knowledge gap and need to establish guidelines for reviewing these studies. This present work, written from the machine learning practitioner standpoint, follows a similar approach to our previous paper related to segmentation. In this series, the committee will address best practices to follow in AI-based studies and present the required sections with examples and discussion of requirements to make the studies cohesive, reproducible, accurate, and self-contained. This entry in the series focuses on image classification. Elements like dataset curation, data pre-processing steps, reference standard identification, data partitioning, model architecture, and training are discussed. Sections are presented as in a typical manuscript. The content describes the information necessary to ensure the study is of sufficient quality for publication consideration and, compared with other checklists, provides a focused approach with application to image classification tasks. The goal of this series is to provide resources to not only help improve the review process for AI-based medical imaging papers, but to facilitate a standard for the information that should be presented within all components of the research study. |
| Keywords: | classification; medical imaging; artificial intelligence; checklist; best practices; paper review |
| Journal Title: | Journal of Imaging Informatics in Medicine |
| ISSN: | 2948-2925 |
| Publisher: | Springer |
| Publication status: | Online ahead of print |
| Date Published: | 2025-01-01 |
| Online Publication Date: | 2025-01-01 |
| Language: | English |
| ACCESSION: | WOS:001501943100001 |
| DOI: | 10.1007/s10278-025-01548-w |
| PROVIDER: | wos |
| Notes: | Article; Early Access -- Source: Wos |
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