Using generative AI to investigate medical imagery models and datasets.

ABSTRACT:

Background

AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts.

Methods

In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We prop

SUBMITTER: Lang O 

PROVIDER: S-EPMC10993140 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature