Importance and Aims: Diabetic microvascular complications significantly impact morbidity
and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in …

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical
decisions more transparent, reliable, and ethically compliant. Despite its promise, the …

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators
reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of …

Abstract Background Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and
family doctors are called to help diabetic patients manage this chronic disease, also with …

Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound
imaging separately, a significant gap exists regarding the development of integrated models …

Background Peripheral vascular disease (PVD) is a common complication in patients with
type 2 diabetes mellitus (T2DM). Early detection or prediction the risk of developing PVD is …

Currently, many individuals are experiencing diabetes, which is attributed to work-related
stress and unhealthy lifestyles. Often, people are only aware of their health status once …

Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL).
Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for …

Background Machine learning is increasingly recognized as a viable approach for
identifying risk factors associated with diabetic kidney disease (DKD). However, the current …

Objective The purpose of this study was to conduct a systematic investigation of the potential
of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and …