Latest AI and machine learning research in primary care for healthcare professionals.
Breast cancer detection remains a significant challenge in medical diagnostics. Traditional diagnostic methods are time-consuming, unable to detect complex patterns in medical images, and achieve only moderate accuracy with high false-positive rates. The study aims to identify the most effective and efficient algorithm for clinical application that reduces the rates of false positives and false ne...
BACKGROUND: Periodontitis is a chronic inflammatory disease driven by host immune dysregulation. However, the specific genetic regulatory mechanisms underlying this disease remain unclear. Identifying key molecular targets is crucial for precise therapeutic intervention. METHODS: This study integrated genome-wide association study (GWAS) summary statistics from the Gene-Lifestyle Interactions in D...
Cardiovascular diseases remain the world's leading cause of death-yet the molecular mechanisms linking genetic variation to clinical outcomes are stil...
INTRODUCTION: Adult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities ...
BACKGROUND: Mild cognitive impairment is widely recognized as a high-risk state associated with a progression to dementia. Although previous studies h...
BACKGROUND: Label-free vibrational spectroscopic techniques (Raman spectroscopy) combined with machine learning (ML) methodologies have huge potential...
BACKGROUND: Systematic reviews require reviewers to decide on the eligibility of large numbers of articles derived from database searches. To accelera...
OBJECTIVE: To describe an exploratory initial experience with two complementary digital platforms consisting of an AI-based telemedicine system and an...
BACKGROUND: Most people with dementia reside in the community and are cared for by family members. Family caregivers play an essential role in support...
BACKGROUND: Health care systems are increasingly confronted with the challenge of managing complex clinical processes. One proposed solution is a pati...
Gestational diabetes mellitus, often known as GDM, is a major health issue that causes complications for mothers and requires patient data prediction ...
BACKGROUND: Diabetes is a chronic condition that arises when the body cannot effectively regulate blood glucose levels, either due to insufficient ins...
OBJECTIVE: Diabetic foot complications (DFCs) are common diabetes complications. Existing tools for predicting incident DFCs remain insufficient. This...
BACKGROUND: Pancreatic cancer is diagnosed at advanced stages in diabetes patients. Existing prediction models require complete historical data and fo...
PURPOSE: To develop and validate a machine-learning model using systemic and ophthalmic parameters that predicts sleep-disordered breathing (SDB) in p...
Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite tradition...
The persistent global rise in atherosclerotic cardiovascular disease (ASCVD) challenges the effectiveness of current risk factor-based algorithms in p...
CONTEXT: The importance of utilizing continuous glucose monitoring (CGM) to optimize glycemic profiles and thereby prevent the onset of diabetic compl...
Cities increasingly require digital services that convert urban climate signals into maintenance decisions with measurable environmental and economic ...
PURPOSE: Artificial intelligence (AI) is increasingly explored as a complement to radiologists in population-based breast cancer screening, yet optima...