Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification.
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-revie...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 39; no. 2; pp. 1041 - 1052 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
| Format: | tables/charts Journal Article |
| Published: |
Springer Nature
Apr2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193167805&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193167805 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2026 vid: 39 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193167805 189894333 193167805 193167805 10.1007/s10278-025-01548-w 193167805 ppf: 1041 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification. aug: au: Kline, Timothy L. Kitamura, Felipe Warren, Daniel Pan, Ian Korchi, Amine M. Tenenholtz, Neil Moy, Linda Gichoya, Judy Wawira Santos, Igor Moradi, Kamyar Avval, Atlas Haddadi Alkhulaifat, Dana Blumer, Steven L. Hwang, Misha Ysabel Git, Kim-Ann Shroff, Abishek Stember, Joseph Walach, Elad Shih, George Langer, Steve G. affil: https://ror.org/02qp3tb03 Department of Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Artificial Intelligence Utilization Image Processing, Computer Assisted Classification Diagnostic Imaging Checklists Machine Learning Data Curation Software Deep Learning Reproducibility of Results Manuscripts Privacy and Confidentiality Image Interpretation, Computer Assisted Sensitivity and Specificity ab: 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. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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