Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials

In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within the healthcare sector and demonstrate referable examples for diagnostic, therapeutic, and prognostic tasks. We encourage researchers to move beyond retrospective and within-sample validation, and step into the practical implementation at the bedside rather than leaving developed machine learning models in the dust of archived literature.

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Publication Details

Journal
Health care science
Published
2024-10-01
DOI
https://doi.org/10.1002/hcs2.114
Citations
19
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
2.29

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article

Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials

Han Yuan
19 citations
Health care science
Artificial Intelligence in Healthcare and Education
2.29
article

Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials

Han Yuan
article en
19 citations

Abstract

In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within the healthcare sector and demonstrate referable examples for diagnostic, therapeutic, and prognostic tasks. We encourage researchers to move beyond retrospective and within-sample validation, and step into the practical implementation at the bedside rather than leaving developed machine learning models in the dust of archived literature.

Health care scienceVol. 3(5)
Duke-NUS Medical School (SG)
Duke-NUS Medical School
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
2.29
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