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
Online-Zugang:	Volltext
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