Generalization in medical AI: a perspective on developing scalable models

Fuente: arXiv
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Auteurs principaux: Zvuloni, Eran, Celi, Leo Anthony, Behar, Joachim A.
Format: Preprint
Publié: 2023
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author Zvuloni, Eran
Celi, Leo Anthony
Behar, Joachim A.
author_facet Zvuloni, Eran
Celi, Leo Anthony
Behar, Joachim A.
contents The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications. A three-level scale is introduced to characterize out-of-distribution generalization performance of medical AI models. This scale addresses the diversity of real-world medical scenarios as well as whether target domain data and labels are available for model recalibration. It serves as a tool to help researchers characterize their development settings and determine the best approach to tackling the challenge of out-of-distribution generalization.
format Preprint
id arxiv_https___arxiv_org_abs_2311_05418
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Generalization in medical AI: a perspective on developing scalable models
Zvuloni, Eran
Celi, Leo Anthony
Behar, Joachim A.
Machine Learning
Artificial Intelligence
The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications. A three-level scale is introduced to characterize out-of-distribution generalization performance of medical AI models. This scale addresses the diversity of real-world medical scenarios as well as whether target domain data and labels are available for model recalibration. It serves as a tool to help researchers characterize their development settings and determine the best approach to tackling the challenge of out-of-distribution generalization.
title Generalization in medical AI: a perspective on developing scalable models
topic Machine Learning
Artificial Intelligence
url https://arxiv.org/abs/2311.05418