This paper is in the following e-collection/theme issue:

Editorial (50) Artificial Intelligence (2129) Machine Learning (2076) Digital Science, Open Science (91) Digital Health Reporting Standards, Quality and Transparency in e-Research (132) Development and Evaluation of Research Methods, Instruments and Tools (744) Knowledge Translation and Implementation Science (513)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52508, first published .
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

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

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

Authors of this article:

Khaled El Emam1, 2 Author Orcid Image ;   Tiffany I Leung3, 4 Author Orcid Image ;   Bradley Malin5 Author Orcid Image ;   William Klement2 Author Orcid Image ;   Gunther Eysenbach3, 6 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

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  2. Lian R, Tang H, Chen Z, Chen X, Luo S, Jiang W, Jiang J, Yang M. Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features. Aging Clinical and Experimental Research 2025;37(1) View
  3. Loh D, Hill E, Liu N, Dawson G, Engelhard M. Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis. JMIR AI 2025;4:e62985 View
  4. Smiley A, Villarreal-Zegarra D, Reategui-Rivera C, Escobar-Agreda S, Finkelstein J. Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis. Frontiers in Oncology 2025;15 View
  5. Saygitov R, Sinitsyn V. Good publication practices: declaration, approval, and now enforcement of reporting standards. Digital Diagnostics 2025;6(2):345 View
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  9. Xiong A, Wang J, Wang Z, Qi D, Yu Y, Xia L, Gao J. Establishment and comparison of three fear of progression risk prediction models for gynecological malignancies patients based on machine learning. Frontiers in Oncology 2025;15 View