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arXiv · 2311.05418

Generalization in medical AI: a perspective on developing scalable models

Eran Zvuloni·Leo Anthony Celi·Joachim A. Behar
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Abstract

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.

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Eran Zvuloni, Leo Anthony Celi, Joachim A. Behar. 2023-11-09. Generalization in medical AI: a perspective on developing scalable models. https://arxiv.org/abs/2311.05418

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