Primary Care

Endocrine hypertension in 2025: how AI and multiomics are revolutionizing detection.

INTRODUCTION: Endocrine hypertension (EH) represents a small yet clinically significant subset of secondary hypertension with curative potential. Recent advances in multiomics and artificial intelligence (AI) are transforming the diagnostic and therapeutic paradigms of EH, enabling earlier detection, precise subtyping, and personalized treatment strategies. AREAS COVERED: This review integrates ev...

Artificial Intelligence for radiation dose reduction in computed tomography: a narrative synthesis of clinical evidence from 2020 to 2025.

Computed tomography (CT) is essential to modern clinical practice but contributes substantially to population radiation exposure, particularly in oncology, paediatric and screening pathways. In line with the ALARP principle and IR(ME)R requirements, there is growing interest in the use of artificial intelligence (AI) to reduce dose without compromising diagnostic performance. This work synthesised...

Exploration of an interpretable machine learning-based screening manner for low muscle mass among Chinese community-dwelling older adults using routine physical examination information.

BACKGROUND: With the accelerating aging of the global population, muscle health issue occurs commonly as an age-related process in older people. The c...

Hypoglycemia problem-solving ability as a key predictor of severe hypoglycemia: a machine learning approach in adults with type 1 diabetes.

AIMS/INTRODUCTION: Severe hypoglycemia (SH) is a major complication in adults with type 1 diabetes mellitus (T1DM). The multifactorial etiology of T1D...

Integrating qHTS and QSAR Models to Identify Safe GPCR-Targeted Compounds: A Focus on hERG-Dependent Cardiotoxicity.

G-protein-coupled receptors (GPCRs) are a diverse family of seven-transmembrane domain receptors that play pivotal roles in various physiological and ...

Primary Care Physician Perspectives on Artificial Intelligence for Dementia Screening: A Qualitative Study.

BACKGROUND: Automated approaches to cognitive impairment screening may soon achieve sufficient levels of accuracy for clinical implementation but they...

An online machine learning model for predicting medication adherence in hypertensive patients: data from the China health and retirement longitudinal study (CHARLS).

INTRODUCTION: Hypertension, a leading global cause of death with high prevalence and poor control, faces a critical issue of poor medication adherence...

Enhancing health monitoring in smart offices: a multi-layered digital twin approach.

Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and c...

A machine learning model for optimizing treatment of patients with poorly controlled type 2 diabetes.

BACKGROUND: Cardiorenal-protective sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) lack se...

Study protocol for evaluating automation of systematic review processes with EPPI-Reviewer and Copilot 365 in updating the cataract evidence gap map.

BACKGROUND: The process of developing and updating an evidence gap map (EGM) is based on the principles of systematic reviews and requires extensive t...

Investigating feature-engineered predictors for systolic blood pressure changes in an mHealth-based disease management program.

Mobile health (mHealth)-based disease management programs enable continuous monitoring of blood pressure (BP) and related health behaviors. Feature en...

Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults.

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Incorporating global-local tissue changes to predict future breast cancer from longitudinal screening mammograms.

Early detection of breast cancer (BC) through mammography screening is critical for reducing mortality and improving patient outcomes. However, full-p...

Epidemiological Trajectories and Quality of Care Disparities of MASLD in Asia, 1990-2023.

BACKGROUND: To characterize temporal and geographic patterns of metabolic dysfunction-associated steatotic liver disease (MASLD) across Asia from 1990...

Exploring the feasibility of near-infrared spectroscopy and machine learning for detecting cardiovascular diseases and diabetes mellitus in fingernails.

Cardiovascular diseases (CVDs) and diabetes mellitus (DM) are significant conditions that impact lives around the globe. Frequently employed methods f...

Deep-Q-Network in Multiview Ensemble Learning to Predict Anti-diabetic Peptide and Diabetic Types.

Diabetes mellitus disease has become a significant human health concern globally, causing cardiovascular complications, kidney disease, cerebrovascula...

Emerging Advanced Electronic Packaging Materials for Thermal Management in Power Electronics.

Current research on integrated circuits and power electronics is rapidly advancing toward miniaturization, high power density, and multi-chip integrat...

Emerging Roles of Multiparametric Biodosimetry and Artificial Intelligence in Advancing Radiation Dose Assessment for Triage.

Biodosimetry plays a crucial role in radiation emergency preparedness and response by enabling efficient allocation of medical resources through prior...

Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer.

Insulin resistance is suggested to be a risk factor for cancer; however, large-scale epidemiological evidence linking insulin resistance to cancer rem...

Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.

Chronic kidney disease (CKD) is a common complication of type 2 diabetes mellitus (T2DM), with limited predictive tools for individualized prognosis, ...