In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Shortcut learning is when a model–eg a cardiac disease classifier–exploits correlations
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …

Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A scoping review was conducted following the Joanna Briggs …

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 …

With the power of parallel processing, large datasets, and fast computational resources,
deep neural networks (DNNs) have outperformed highly trained and experienced human …

The clinical explainability of convolutional neural networks (CNN) heavily relies on the joint
interpretation of a model's predicted diagnostic label and associated confidence. A highly …

The success of deep learning in identifying complex patterns exceeding human intuition
comes at the cost of interpretability. Non-linear entanglement of image features makes deep …