With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

Explainable AI is not a new field. Since at least the early exploitation of CS Pierce's
abductive reasoning in expert systems of the 1980s, there were reasoning architectures to …

The lack of transparency is one of the artificial intelligence (AI)'s fundamental challenges, but
the concept of transparency might be even more opaque than AI itself. Researchers in …

Abstract Neural Networks (NNs) have provided state-of-the-art results for many challenging
machine learning tasks such as detection, regression and classification across the domains …

Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination
and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the …

Breast cancer care is a leading area for development of artificial intelligence (AI), with
applications including screening and diagnosis, risk calculation, prognostication and clinical …

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID‐
19 patients has demonstrated potential for improving clinical decision making and assessing …

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 …

Diagnostic pathology is the foundation and gold standard for identifying carcinomas.
However, high inter-observer variability substantially affects productivity in routine pathology …