A twelve item checklist for clinicians to critically evaluate and design better clinical machine learning studies.
The article discusses data quality issues in clinical projects and helps establish baseline performance standards.
How to examine machine learning workflow and their metrics using statistical measures when reporting model performance.
Highlighting issues of bias, model interpretability, and clinical relevance for more equitable healthcare.
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