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Editorial
. 2024 May 2:26:e52508.
doi: 10.2196/52508.

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

Khaled El Emam  1   2 , Tiffany I Leung  3   4 , Bradley Malin  5 , William Klement  2 , Gunther Eysenbach  3   6
Affiliations
Editorial

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

Khaled El Emam et al. J Med Internet Res. .

Abstract

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

Keywords: artificial intelligence; diagnostic models; editorial policy; machine learning; predictive models; prognostic models; reporting guidelines.

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Conflict of interest statement

Conflicts of Interest: KEE and BM are co–editors-in-chief of JMIR AI. KEE is the cofounder of Replica Analytics, an Aetion company, and has financial interests in the company. TIL is the scientific editorial director at JMIR Publications, Inc. GE is the executive editor and publisher at JMIR Publications, Inc, receives a salary, and owns equity.

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