Twelve key challenges in medical machine learning and solutions

The utility of machine learning in biomedicine is being investigated in various contexts, including for diagnostic and interpretive purposes for imaging modalities, quantifying disease risk, and processing text from physician and patient reports. To best facilitate the potential of machine learning,...

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Bibliographic Details
Published in:	Intelligence-based medicine Vol. 6; p. 100068
Main Authors:	Ellis, Randall J., Sander, Ryan M., Limon, Alfonso
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 2022
Subjects:	Baseline models Imbalanced datasets Machine learning Model and label uncertainty Performance metrics Reproducibility Model and label uncertainty Performance metrics Reproducibility Baseline models Imbalanced datasets Machine learning
Online Access:	Get full text
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Summary:	The utility of machine learning in biomedicine is being investigated in various contexts, including for diagnostic and interpretive purposes for imaging modalities, quantifying disease risk, and processing text from physician and patient reports. To best facilitate the potential of machine learning, clinicians and computational scientists must inform one another about the nature of their clinical challenges and available methods for solving them, respectively. To this end, clinicians need to critically evaluate machine learning studies conducted to solve relevant problems in medicine. This article serves as a checklist for clinicians to understand and appraise machine learning studies and help facilitate productive conversations between the clinical and data science communities to improve human health. •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.
ISSN:	2666-5212 2666-5212
DOI:	10.1016/j.ibmed.2022.100068