In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

The American Diabetes Association (ADA)“Standards of Care in Diabetes” includes the
ADA's current clinical practice recommendations and is intended to provide the components …

The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …

Eye health and vision have widespread and profound implications for many aspects of life,
health, sustainable development, and the economy. Yet nowadays, many people, families …

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …

Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …

Diabetic retinopathy (DR) is the major ocular complication of diabetes mellitus, and is a
problem with significant global health impact. Major advances in diagnostics, technology …

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment.
To facilitate the screening process, we develop a deep learning system, named DeepDR …

Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …