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Updated: Jun 27, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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

Khaled El Emam^1,2, Tiffany I Leung^3,4, Bradley Malin⁵

¹School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.

Journal of Medical Internet Research

|May 2, 2024

Summary

This summary is machine-generated.

JMIR Publications emphasizes reporting guidelines for machine learning (ML) studies. The Consolidated Reporting of Machine Learning Studies (CREMLS) checklist enhances transparency and rigor in scientific reporting.

Keywords:

artificial intelligence diagnostic models editorial policy machine learning predictive models prognostic models reporting guidelines

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Last Updated: Jun 27, 2025

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Scientific Publishing

Background:

Increasing submission of machine learning (ML) models in medical research journals.
Challenges in ensuring quality and completeness of ML study reporting during peer review.
Need for standardized reporting guidelines to improve manuscript consistency.

Discussion:

JMIR Publications' policy on applying reporting guidelines for all manuscript types.
Focus on specific reporting standards for ML studies within JMIR Publications.
Introduction of the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines.

Key Insights:

Reporting guidelines are crucial for consistent quality in scientific publications.
The CREMLS checklist provides a framework for transparent and rigorous ML study reporting.
Adherence to CREMLS can prevent missing information and enhance reproducibility.

Outlook:

Encouraging authors and journals to adopt the CREMLS checklist.
Promoting wider use of CREMLS to improve the overall quality of ML research reporting.
Potential for CREMLS to become a standard in medical informatics publishing.