What question did this study set out to answer?

This review aims to examine potential applications of machine learning in medicine and the challenges impeding its implementation.

November 16, 2015CirculationOpen Access

Machine Learning in Medicine

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Key result

Medical machine learning fails to meaningfully impact clinical care despite thousands of published papers.

Key Points

This review aims to examine potential applications of machine learning in medicine and the challenges impeding its implementation.
Literature review on the applications of machine learning in health care.
Identification of barriers that limit the clinical use of machine learning approaches.
Highlighted several examples where machine learning has been applied in health care, yet few have made a significant clinical impact.
Identified obstacles such as insufficient data integration and the gap between research and clinical practice.

Design

Review

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Implication

Clinicians should await rigorous prospective validation before adopting ML tools; challenges assumptions of rapid translation from abundant research.

Cite This Study

Rahul C. Deo (2015) studied this question. Despite thousands of papers on machine learning in medicine, very few have significantly impacted clinical care compared to its success in other industries.

synapsesocial.com/papers/6989b0814811b64c48e40a2a https://doi.org/10.1161/circulationaha.115.001593

Topics

Artificial intelligence in cardiology

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Implication

Clinicians should await rigorous prospective validation before adopting ML tools; challenges assumptions of rapid translation from abundant research.

Machine Learning in Medicine

Key Points

Discussion

Implication

Cite This Study

Topics

Machine Learning in Medicine

Key Points

Discussion

Implication

Cite This Study

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