Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine.

ABSTRACT: AI is now a cornerstone of modern dataset analysis. In many real world applications, practitioners are concerned with controlling specific kinds of errors, rather than minimizing the overall number of errors. For example, biomedical screening assays may primarily be concerned with mitigating the number of false positives rather than false negatives. Quantifying uncertainty in AI-based predictions, and in particular those controlling specific kinds of errors, remains theoretically and practically challenging. We develop a strategy called multidimensional informed generalized hypothesis testing (MIGHT) which we prove accurately quantifies uncertainty and confidence given sufficient data, and concomitantly controls for particular error types. Our key insight was that it is possible to integra

SUBMITTER: Curtis SD 

PROVIDER: S-EPMC12402999 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature