Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases.
Journal:
JCO clinical cancer informatics
Published Date:
Oct 1, 2022
Abstract
PURPOSE: Artificial intelligence (AI) models for medical image diagnosis are often trained and validated on curated data. However, in a clinical setting, images that are outliers with respect to the training data, such as those representing rare disease conditions or acquired using a slightly different setup, can lead to wrong decisions. It is not practical to expect clinicians to be trained to discount results for such outlier images. Toward clinical deployment, we have designed a method to train cautious AI that can automatically flag outlier cases.