Objective: We performed a comprehensive review of the literature to better understand the
trust dynamics between medical artificial intelligence (AI) and healthcare expert end-users …

Due to the success of artificial intelligence (AI) applications in the medical field over the past
decade, concerns about the explainability of these systems have increased. The reliability …

AI-powered image analysis and pattern recognition algorithms (IAPRA) are renowned for
their capacity to identify concrete flaws, assess strength characteristics, and anticipate the …

Problem definition: Effective hypertension management is critical to reducing the
consequences of atherosclerotic cardiovascular disease, a leading cause of death in the …

Blood pressure (BP) estimation is one of the most popular and long-standing topics in health-
care monitoring area. The utilization of machine learning (ML) and deep learning (DL) for BP …

Background: Glaucoma is a group of chronic diseases that cause progressive damage to the
optic nerve, resulting in irreversible and potentially debilitating visual loss. A unified …

The advent of machine learning in personalized medicine has revolutionized the healthcare
industry by providing an enhanced diagnosis and treatment regimen to patients based on …

The increased utilization of disruptive health and biomedical informatics technologies, such
as artificial intelligence (AI), has accelerated medical operations from patient-centered …

Artificial intelligence in healthcare has the potential to enhance diagnostics, patient care,
and medical research. However, trust in AI-driven decision-making processes is crucial as AI …

The ability of deep learning (DL) to improve the practice of medicine and its clinical
outcomes faces a looming obstacle: model interpretation. Without description of how outputs …