Primary Care

ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening

Treating patients with combinations of drugs reduces the risk of resistance to any individual drug. Finding effective combinations is difficult because the large search space makes combinatorial screens prohibitively expensive, time consuming, and often technically infeasible. Predictive models can fill this gap, yet existing methods typically require molecular profiling of each sample and per-coh...

Label-Free Parkinson's Disease Screening from Face and Voice through Mechanistic Interpretability

Parkinson's disease (PD) is the second most common neurodegenerative disorder. Typical machine learning screening methods require PD labels, but the available data is limited by privacy concerns and the need for expert annotation. We propose a label-free face-plus-voice PD screen built entirely on frozen pretrained encoders--a face-expression Vision Transformer and HuBERT--in which no PD label tou...

Deep Learning of Fluorescence Lifetime Imaging Ophthalmoscopy for Type 2 Diabetes Classification

Purpose: To evaluate whether fluorescence lifetime imaging ophthalmoscopy (FLIO) combined with deep learning can detect metabolic signatures for class...

TLNM: Externally Validated Tooth Detection, Numbering and Segmentation from Smartphone Photographs Using Mask R-CNN

Oral health issues affect billions globally, but the cost and limited access to professional dental care hinder preventive oral healthcare. Research r...

From Precision Medicine to Precision Education: A Vision for AI-Powered Student Digital Twins, Preventive Student Success, and Career-Aligned Academic Pathways

Higher education remains largely reactive in its approach to student success. Institutions frequently identify academic problems only after students h...

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently,...

NeuroAid An Open-Data Multimodal Screening Framework for Parkinson's and Depression Risk Estimation

Neurological and mental-health conditions such as Parkinson's disease (PD) and major depressive disorder (MDD) impose a substantial and growing global...

Architectural Safety Mechanisms for Multi-Agent Clinical LLM Systems Under Knowledge Base Distribution Shift

Objective: To evaluate whether multi-agent LLM architectures with explicit safety verification maintain guideline compliance when their clinical knowl...

Self-supervised DXA representations encode multi-system disease risk, biological aging and heritability

Whole-body dual-energy X-ray absorptiometry (DXA) scans are routinely acquired to measure bone density and regional body composition, leaving their sp...

A PRISMA-Aligned Agentic Framework for Medical Systematic Reviews and Evidence Synthesis

Medical systematic reviews are central to evidence-based medicine, but they remain slow, labor-intensive, and difficult to maintain under the full Pre...

Nanoparticle Networks for Neuromorphic Computing

Physical computing leverages complex dynamical systems for energy-efficient data processing. In this work, we present a neuromorphic architecture base...

Fine-Grained Emotional Characterization of Dementia Caregivers in Online Support Communities Using Large Language Models

Background: Dementia caregiving carries substantial emotional and psychological consequences, but most evidence comes from structured surveys and inte...

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each indiv...

OrthKD: Extracting Generalized Clinical Knowledge from Heterogeneous Teachers for Lightweight Deployment

Deploying diabetic retinopathy (DR) screening models in primary care requires edge-efficient systems that remain accurate, safe, and reliable under do...

VetClaw: An Edge-Cloud Multimodal Agentic System for Veterinary Disease Screening

We present VetClaw, an edge-cloud multimodal agentic system for early veterinary disease screening. VetClaw uses a camera module as an edge sensing de...

A digital health approach for identifying polyendocrine metabolic ovarian syndrome using machine learning and body temperature

Background Polyendocrine Metabolic Ovarian Syndrome (PMOS), formerly known as Polycystic Ovary Syndrome (PCOS), is a prevalent endocrine disorder with...

Bayesian Feature Extraction using Gaussian and Diffused-gamma Priors for High Dimensional Spatio-Temporal Data

High-dimensional data with sparse structure and spatio-temporal dependence arise in many scientific domains. We develop a Bayesian feature-extraction ...

Precision Management of Fludrocortisone-Related Hypertension Risk in Congenital Adrenal Hyperplasia: A Machine Learning Approach to Personalized Dosing

Congenital adrenal hyperplasia (CAH) is a rare inherited disorder requiring lifelong hormone replacement therapy. Excessive hormone replacement poses ...

Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reduci...

GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, partic...