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.
View on arXiv@article{zvuloni2025_2311.05418,
title={ Generalization in medical AI: a perspective on developing scalable models },
author={ Eran Zvuloni and Leo Anthony Celi and Joachim A. Behar },
journal={arXiv preprint arXiv:2311.05418},
year={ 2025 }
}