Performance metrics for machine learning solutions in laboratory medicine.
Machine learning–based solutions to laboratory medicine problems have become commonplace in literature, but real-world implementations remain rare, in no small part because of the substantial investment required to incorporate such solutions into routine clinical care. A crucial step in advancing a...
| Publicado en: | Laboratory Medicine Vol. 56; no. 6; pp. 597 - 608 |
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| Autores principales: | , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Machine learning–based solutions to laboratory medicine problems have become commonplace in literature, but real-world implementations remain rare, in no small part because of the substantial investment required to incorporate such solutions into routine clinical care. A crucial step in advancing a machine learning solution from proof of concept into clinical application is a robust and comprehensive evaluation of its performance. In this review, we discuss the common methods, best practices, and potential pitfalls in evaluating machine learning–based solutions to clinical laboratory problems. |
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