Generalization in medical AI: a perspective on developing scalable models
This work addresses the issue of translating medical AI research into practical applications by providing a framework for evaluating generalization, though it is incremental as it focuses on characterization rather than new methods.
The paper tackles the problem of generalization in medical AI by introducing a three-level scale to characterize out-of-distribution performance, helping researchers assess and address challenges in diverse real-world clinical scenarios.
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.