Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases.

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.

Effect of InAlGaAs and GaAs combination barrier thickness on the duration of dot formation in different layers of stacked InAs/GaAs quantum dot heterostructure grown by MBE.

Multilayer stacks of quantum dots (QDs) (10 periods) with a combination barrier layer of In0.21Al0.21 Ga0.