Primary Care

Latest AI and machine learning research in primary care for healthcare professionals.

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Glucagon-like peptide-1 receptor agonist initiation and risk of clinically recorded Alzheimer's disease-type dementia in older adults with type 2 diabetes: a target trial emulation using causal machine learning

Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 diabetes. Despite established metabolic, cardiovascular, and renal benefits, it remains uncertain whether GLP-1 receptor agonists are associated with longer clinically recorded Alzheimer's disease (AD)-type dementia-free survival than sulfonylureas (SU...

Development and multi-dataset evaluation of a unified single-view deep-learning model for the right heart: four-chamber segmentation, biventricular ejection fraction, deformation, and pulmonary-hypertension prediction from the apical four-chamber echocardiogram

Background: Right ventricular (RV) function predicts survival in pulmonary hypertension (PH) and other cardiovascular diseases, yet echocardiographic AI has largely focused on the left ventricle (LV). Objectives: To develop and evaluate PH-ECHO-AI, a unified deep learning model performing four-chamber segmentation, landmark localisation, biventricular ejection fraction (EF) estimation, deformation...

Phthalate exposure and obesity in US adults: a small but robust association, and three leakage mechanisms that inflate it

Background. Phthalates are hypothesised to act as metabolic disruptors, and machine learning applied to the National Health and Nutrition Examination ...

KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research

Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant...

The urinary-metabolite-based lung cancer index (uLCI): an interpretable machine-learning risk model for early-stage disease

BackgroundFive-year survival from lung cancer exceeds 60% at stage I-II but falls below 10% once metastasis occurs. Low-dose CT (LDCT) screening reduc...

Geographically Weighted Machine Learning for Spatial Prediction of Cancer Prevalence in the United States: A Mixed Method Approach

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use o...

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCL...

Aug 20 2026 2608.19906v1
Explainable Clinician-Supervised Artificial Intelligence as an Implementation Framework for Cardiovascular-Kidney-Metabolic Population Health: Synthetic Data Validation of the CHAPERONE-CKM Framework

Abstract Background: Cardiovascular-kidney-metabolic (CKM) syndrome is an increasingly prevalent multisystem condition associated with morbidity, frag...

U.S. National Liquefaction Hazard Maps and their Implications for Engineering Practice and Policy

This study introduces U.S. national liquefaction hazard maps (NLHMs) developed using a mechanics-informed, geospatial machine learning model which sur...

Aug 19 2026 2608.19137v1
Spatial and Machine Learning Analysis of Breast and Cervical Cancer Screening Uptake in Ghana: Evidence from the 2022 Ghana Demographic and Health Survey

Abstract Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) ...

Community Learning Ledgers for Cancer Navigation in Small Island Developing States

Importance. Cancer is the second leading cause of death among patients in the Caribbean, where outcomes are associated with delayed clinical navigatio...

Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD Study

Importance: Consumer wearables such as the Apple Watch can record single-lead electrocardiograms (ECGs) but are used mainly to detect rhythm disorders...

NutrIA: Development and Internal Validation of a Hybrid Clinical Decision Support System for Personalized Preventive Nutrition

Background: The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capabl...

Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage a...

Cross-attention and language models reveal the interpretability of functional predictions for the human olfactory receptor family

The attention mechanism offers the possibility for data-driven discovery of biological principles. However, for important protein families such as hum...

Making Broad Evidence Synthesis Feasible: An LLM Screening Agent for Meta-Analyses Applied To Suicide Prevention

Importance. Systematic reviews and meta-analyses inform suicide-prevention policy and practice, but broad database searches are difficult to screen ma...

Population-scale analysis reveals limited and non-generalizable associations between the gut microbiome and obesity in Asian adults

Abstract Background The gut microbiome has been widely studied in the context of obesity, and yet the reported associations vary widely across populat...

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is const...

Aug 13 2026 2608.12805v1
A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations

Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a n...

GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning

Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we intro...

Aug 12 2026 2608.12185v1
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