Primary Care

Targeted use of large language models for EHR-based computable phenotyping.

OBJECTIVE: Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large language models (LLMs) can extract clinically rich information from unstructured notes, routine application to all patients is computationally expensive. We evaluated whether uncertainty-guided selective use of LLMs can improve phenotyping accuracy w...

Comparative assessment of diagnostic agreement between artificial intelligence and general practitioners in diabetic retinopathy screening using non-mydriatic fundus photography.

AIMS: To assess the diagnostic agreement between an artificial intelligence (AI) system and general practitioners (GPs) interpreting fundus photographs for diabetic retinopathy (DR) screening, using the ophthalmologists' assessment as the reference standard. METHODS: We performed a cross-sectional study of 500 primary care patients with type 2 diabetes (T2DM). Each underwent two 45° non-mydriatic ...

Artificial intelligence for research capacity strengthening: Two reviews and a pathway to shift power in global health.

Significant disparities persist in how researchers from low- and middle-income countries (LMICs) and high-income countries (HICs) participate in agend...

Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.

PURPOSE: To determine whether a high-quality, prospectively curated dataset can, by itself, enable the development of robust and clinically effective ...

Integrated multi-omics analysis identifies TBC1D10C as an NK- and T cell-associated diagnostic biomarker in childhood obesity.

Childhood obesity is a growing global health crisis associated with an increased risk of metabolic and cardiovascular diseases in adulthood. While acc...

Artificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives.

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, emphasizing the ongoing need for effective and scalable ...

MAISNet: A Multi-Species Integrated Graph Neural Network for Acetylcholinesterase Inhibitor Screening.

MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as alzheimer's disease (AD). Although existing dru...

Deep learning in population-based screening for glaucoma.

BACKGROUND/AIMS: Deep learning algorithms have shown promise for glaucoma detection using retinal imaging. The Northern Finland Birth Cohort Eye Study...

Artificial intelligence in public health-challenges and opportunities.

AI solutions are frequently presented as promising solutions to a wide range of global challenges, including public health. However, the potential the...

Prompt engineering of large language models for paper screening in medical meta-analyses and systematic reviews: A prospective comparative study.

Interest in large language models (LLMs) as a tool for meta-analyses and systematic reviews (MA/SRs) is growing. We prospectively developed 515 unique...

Decision support systems (DSS) for predicting hypertensive events using real-world telemonitoring data.

OBJECTIVE: This study contributes to the limited literature on applying machine learning (ML) to telemonitoring data for timely decision support syste...

Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic, nonspecific inflammatory disorder affecting the gastrointestinal tract. The condition's pat...

Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis.

BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approache...

AI-Driven Speech Analysis for Mental Health Prediction in Individuals with Voice Disorders.

BACKGROUND AND OBJECTIVE: Mental health disorders are common among individuals with voice disorders, yet applications of AI-driven speech analysis in ...

Integrated multi-omics and machine learning identify Casp8 and Pycard as central regulators of pyroptosis and necroptosis in spinal cord injury.

Programmed cell death pathways exacerbate secondary damage after spinal cord injury, yet their shared regulators and tractable therapeutic points rema...

Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily drive...

Fully Automatic Diabetic Wound Segmentation Using Lightweight Deep Convolutional Neural Networks.

Diabetic foot ulcers, resulting from neuropathic and/or vascular complications in patients with diabetes mellitus, pose a major global health challeng...

Gated Backbone Fusion with Transformer Encoder for Diabetic Foot Osteomyelitis Screening and Localization in Radiographs.

Diabetic foot osteomyelitis (DFO) is a leading cause of lower-extremity complications in individuals with diabetes, and timely, accurate screening is ...

User Experience and Early Clinical Outcomes of a Mental Wellness Chatbot for Depression and Anxiety: Pilot Evaluation Mixed Methods Study.

BACKGROUND: Artificial intelligence-powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological supp...

Deep learning magnetic resonance imaging algorithm for differentiating metastatic vertebral fractures.

BACKGROUND CONTEXT: Distinguishing malignant metastatic lesions from benign osteoporotic vertebral compression fractures (VCFs) is a major diagnostic ...