Out-of-Distribution Detection as a Risk-Control Strategy for Medical Classification Machine Learning Models.

ABSTRACT: Recent advances in artificial intelligence (AI) and machine learning (ML) algorithms have significantly broadened the scope in which these algorithms are deployed. The use of these algorithms in high-stakes medical contexts requires that the algorithms have appropriate guardrails to ensure their performance. One such guardrail involves using out-of-distribution (OOD) detection algorithms, which detect if observations are unlikely to be sampled from the model's training distribution. Since these observations are not observed/rare in the training data, the model is likely to be unreliable when performing on these observations. In the medical context, identifying which patients are OOD may improve the performance of the model by filtering out patients on which the model has not been properly

SUBMITTER: Weng C 

PROVIDER: S-EPMC12547361 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature