Artificial intelligence (AI) can acquire characteristics that are not yet known to humans
through extensive learning, enabling to handle large amounts of pathology image data …

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular
diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and …

The advancements in genomics and biomedical technologies have generated vast amounts
of biological and physiological data, which present opportunities for understanding human …

Importance Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic
(EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such …

Objectives To develop a computed tomography (CT) radiomics-based interpretable machine
learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors …

The integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the
field, offering significant advancements in diagnostic accuracy and clinical efficiency …

This review synthesizes the literature on explaining machine-learning models for digital
health data in precision medicine. As healthcare increasingly tailors treatments to individual …

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI
models and is important in building trust in model predictions. XAI explanations themselves …

Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for
systemic disease prediction using retinal imaging. The retina's unique features enable non …

Background Machine learning (ML) risk prediction models, although much more accurate
than traditional statistical methods, are inconvenient to use in clinical practice due to their …