On evaluation metrics for medical applications of artificial intelligence

Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these...

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Veröffentlicht in:Scientific reports Jg. 12; H. 1; S. 5979 - 9
Hauptverfasser: Hicks, Steven A., Strümke, Inga, Thambawita, Vajira, Hammou, Malek, Riegler, Michael A., Halvorsen, Pål, Parasa, Sravanthi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 08.04.2022
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/1046
692/308
692/4020
Artificial Intelligence
Benchmarking
Gastroenterology
Humanities and Social Sciences
Humans
Machine Learning
multidisciplinary
Science
Science (multidisciplinary)
Software
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-09954-8