Artificial intelligence has the potential to transform many application domains fundamentally.
One notable example is clinical radiology. A growing number of decision-making support …

Healthcare providers are increasingly using machine learning to predict patient outcomes to
make meaningful interventions. However, despite innovations in this area, deep learning …

Explainable models in Artificial Intelligence are often employed to ensure transparency and
accountability of AI systems. The fidelity of the explanations are dependent upon the …

Estimating the mortality of patients plays a fundamental role in an intensive care unit (ICU).
Currently, most learning approaches are based on deep learning models. However, these …

Despite the proven effectiveness of Transformer neural networks across multiple domains,
their performance with Electronic Health Records (EHR) can be nuanced. The unique …

As healthcare organizations enter the maintenance and optimization phases of electronic
health record (EHR) implementation, the time has come for us to leverage the vast amounts …

Automated decision making is becoming more and more popular in various domains and
demonstrates high performance capabilities. The growing model complexity has limited the …

Despite the proven effectiveness of Transformer neural networks across multiple domains,
their performance with Electronic Health Records (EHR) can be nuanced. The unique …

Artificial intelligence (AI) is composed of a number of supervised and unsupervised
computational learning techniques. At present, cancer research utilizes some of these …

Artificial intelligence (AI)—intel-ligent technology capable of analyzing, learning, and
drawing predictive insights from data—is transforming many sectors of the global economy; …