Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Predictive machine-learning systems often do not convey the degree of confidence in the
correctness of their outputs. To prevent unsafe prediction failures from machine-learning …

Accurate estimation of predictive uncertainty (model calibration) is essential for the safe
application of neural networks. Many instances of miscalibration in modern neural networks …

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose
melanoma more accurately, however they lack transparency, hindering user acceptance …

Abstract Most current Artificial Intelligence applications are based on supervised Machine
Learning (ML), which ultimately grounds on data annotated by small teams of experts or …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

A model's ability to express its own predictive uncertainty is an essential attribute for
maintaining clinical user confidence as computational biomarkers are deployed into real …

Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …

Background Artificial intelligence (AI) applications in health care have been effective in
many areas of medicine, but they are often trained for a single task using labelled data …