Generalization in medical AI: a perspective on developing scalable models

• URL: http://arxiv.org/abs/2311.05418v2
• Date: Wed, 16 Apr 2025 07:07:16 GMT
• Title: Generalization in medical AI: a perspective on developing scalable models
• Authors: Eran Zvuloni, Leo Anthony Celi, Joachim A. Behar,
• Abstract summary: A three-level scale is introduced to characterize out-of-distribution generalization performance of medical AI models.<n>This scale addresses the diversity of real-world medical scenarios as well as whether target domain data and labels are available for model recalibration.
• Score: 2.6728181032975598
• License: http://creativecommons.org/licenses/by/4.0/
• 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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