Latest AI and machine learning research in primary care for healthcare professionals.
Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through advanced predictive modeling, early disease diagnosis, and ongoing monitoring of conditions such as preeclampsia or gestational diabetes. However, significant challenges in economic valuation persist, including data scarcity, complexity, and the nasce...
High myopia can lead to cataract, glaucoma, retinal detachment, choroidal neovascularisation, and macular degeneration, causing irreversible vision loss. Imaging detects these complications, but population screening is limited by equipment, and specialist availability. Here we show that a machine learning model using routine blood test results identifies people at increased risk of complications r...
OBJECTIVE: Pre-mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co-pathologies. This st...
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three ma...
BACKGROUND: Sarcopenia is characterized by progressive loss of skeletal muscle mass and strength and is associated with increased disability and morta...
Artificial nanomaterials known as nanozymes, which possess inherent enzyme-mimetic characteristics, have transformed environmental research, biomedici...
Effective first-trimester screening for congenital heart disease (CHD) remains an unmet clinical need, hindered by technical constraints and the lack ...
OBJECTIVES: This study examined patterns of clustering of intermediate risk factors for Cardiovascular diseases (CVDs) in a rural South African popula...
BACKGROUND: Cardiovascular disease (CVD) is the most prevalent complication of Type 2 Diabetes Mellitus (T2DM) and a leading cause of mortality in thi...
INTRODUCTION: This study aimed to identify dental pain using machine learning (ML) algorithms in Brazilian adolescents for public health screening pur...
OBJECTIVES: The quantitative analysis of 16-segment left ventricular wall thickness can provide insights into the pathological progression of left ven...
AIM: Systematic reviewing is a time-consuming process that can be aided by artificial intelligence (AI). There are several AI options to assist with t...
Early detection of liver fibrosis in chronic hepatitis B (CHB) patients is crucial for improving their prognosis. This study aims to develop a machine...
BACKGROUND: Observational data are fundamental to medical research but present formidable challenges for causal inference. Machine learning-based caus...
The objective of this scoping review is to examine the nature, extent, and impact of AI-supported interventions that include an AI agent intended to i...
Breast density influences both breast cancer risk and the sensitivity of mammographic screening. Several countries routinely notify women of their bre...
BACKGROUND: Accurate differentiation of common hematologic disorders remains challenging in routine clinical practice and often requires invasive diag...
BACKGROUND: Achieving safe glycemic targets in intensive care remains difficult due to rapidly changing physiology, treatment effects, and measurement...
BACKGROUND AND OBJECTIVE: Renal clear cell carcinoma (ccRCC) is highly heterogeneous, with significant differences in clinical outcomes such as progno...
Screening for early-onset colorectal cancer is a growing public health concern, driven by a rising incidence in younger adults. As trends shift, scree...