AI Reporting Guidelines: How to Select the Best One for Your Research
Open accessAbstract
T he rapid increase in publications related to artificial intelligence (AI) in medical imaging has pinpointed the need for transparent and organized research reporting.Publications on AI research should provide the necessary information to ensure adherence to high scientific standards while allowing the independent reproduction of the research.Reproducibility is necessary to enable clinical translation and adoption of AI algorithms that may otherwise remain on paper.For this purpose, a series of tools have been developed to guide the comprehensive reporting of AI research to promote research reproducibility, adherence to ethical standards, comprehensibility of research manuscripts, and publication of scientifically valid results.The use of such guidelines has been encouraged or mandated by scientific journals to allow appropriate evaluation of research output during the review process.The majority of existing guidelines and checklists allow authors to confirm the inclusion of specific information in each manuscript section: Title, Abstract, Introduction, Methods, Results, and Discussion.The content of these checklists highlights the minimum information a journal requires for the publication of an AI study, providing researchers with guidelines to conduct their research while also easing and standardizing the review process.Existing guidelines or guidelines under development specific to AI include CLAIM (Checklist for Artificial Intelligence in Medical Imaging), STARD-AI (Standards for Reporting of Diagnostic Accuracy Study-AI), CONSORT-AI (Consolidated Standards of Reporting Trials-AI), SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-AI), FUTURE-AI (Fairness Universality Traceability Usability Robustness Explainability-AI), MI-CLAIM (Minimum Information about Clinical Artificial Intelligence Modeling), MINIMAR (Minimum Information for Medical AI Reporting), and RQS (Radiomics Quality Score).In this editorial, we compare these guidelines and their content to help readers find the best guideline for their research.Table 1 provides a high-level overview including a summary of guidelines' availability, purpose, and format and brief information about which manuscript sections they address.Table 2 details specific content for each manuscript section and indicates which checklists include this information.
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Reporting guidelines such as FUTR-AI and TRIPOD-AI can assist authors of AI manuscripts in reporting their studies according to the Fairness principle, promoting the identification of bias sources.
“Reporting guidelines, such as the Fairness Universality Traceability Usability Robustness Explainability-AI and TRIPOD-AI, can assist authors of AI manuscripts in reporting their studies according to the Fairness principle, promoting the identification of bias sources.58, 63, 64”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [58]
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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443 · see citing works
Self-citationLiu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 632 · see citing works
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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update
Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence · doi:10.1148/ryai.240300 · Cited by 570 · see citing works
Self-citationKoçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works
“58,63,64 When dealing with radiomics studies, the CheckList for EvaluA-tion of Radiomics (CLEAR) and METhodolog-ical RadiomICs Score (METRICS) have been developed to evaluate the reporting and methodological study quality.”
Self-citationSpaanderman, D. J., Marzetti, M., Wan, X., Scarsbrook, A. F., Robinson, P., Oei, E. H. G., Visser, J. J., Hemke, R., van Langevelde, K., Hanff, D. F., et al. (2025) · eBioMedicine · doi:10.1016/j.ebiom.2025.105642 · Cited by 26 · see citing works
Koçak, B., Keleş, A., & Köse, F. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.232604 · Cited by 12 · see citing works
- Authors
- Klontzas, M. E., Gatti, A. A., Tejani, A. S., & Kahn, C. E.
- Affiliations
- University of Crete (GR)
- Venue
- Radiology: Artificial Intelligence
- Published
- 2023-05-01
- Volume
- 5
- Issue
- 3
- Pages
- e230055-e230055
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC10245184
- OpenAlex
- W4362606820
- Cited by
- 66
- References
- 3
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 21 · 2023: 11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology Artificial Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- artificial intelligence in medical imaging, reporting guidelines, reproducibility, CONSORT-AI, radiomics quality score
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