Accepted for/Published in: JMIR AI
Date Submitted: Jan 28, 2026
Open Peer Review Period: Jan 28, 2026 - Mar 25, 2026
Date Accepted: Jul 6, 2026
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI model reporting standards in health care: systematic review and consolidation
ABSTRACT
Background:
Artificial intelligence (AI) technologies are currently experiencing significant growth in development and use in health care. The AI model lifecycle includes several stages, all of which should be adequately described. Reporting standards are commonly used for this purpose. However, the plain-text structure of these standards prevents transparent machine-readability of this information.
Objective:
This systematic review aims to consolidate AI model reporting standards from clinical databases and technical documents published since 2016 following PRISMA-2020 guidelines.
Methods:
The inclusion criteria of this systematic review were papers published from 2016 to March 2024 in English and in a peer-reviewed journal with open access. The exclusion criteria were papers in a specific medical domain; papers with incomplete reports; and review papers. The following libraries have been consulted: Scopus and Web of Science databases, MEDLINE and Embase, the IEEE Xplore digital library, and the ACM Digital Library. The results were presented and synthesized in a consolidated list of questions for each AI model lifecycle stage.
Results:
The total number of included studies was 17. The results were presented and synthesized in a consolidated list of questions by grouping questions and ordering them in a logical sequence according to the AI model lifecycle. This led to five groups of questions "Problem identification", "Data collection and processing", "Model development", "Technical validation", "Functional validation". It was showen that only 9 out of 15 FAIR principles were addressed in the consolidated list.
Conclusions:
We furthermore considered the alignment of the consolidated questions with FAIR principles. In future we aim to incorporate the results of our work into a metadata schema of AI models developed according to FAIR principles.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.