Pneumonia

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 interviews that are resource-intensive and may incompletely capture spontaneous, contextual experience. Scalable methods to characterize caregiver experience from real-world narratives are lacking. Objective: To characterize how emotional expression amon...

Multimodal Deep Learning Integrating Electrocardiography and Chest Radiography Enhances Prediction of Incident Regurgitant Valvular Heart Disease

Background: Regurgitant valvular heart disease (rVHD) is a major cause of cardiovascular morbidity. Echocardiography is the diagnostic standard but is resource-intensive for large-scale screening. Electrocardiography (ECG) has shown promise for predicting incident rVHD, yet performance varies across phenotypes, particularly for aortic regurgitation (AR). Chest radiography (CXR) provides complement...

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. C...

SCALPEL: Semantic Cross-modal Alignment via LLM-Powered Encoder Learning for Medical Vision-Language Representation

Vision-language pre-training (VLP) serves as a cornerstone for medical multimodal representation learning. However, existing medical VLP frameworks ar...

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However...

Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed

State-of-the-art toxicity detectors for text-to-image generation adopt a one-size-fits-all approach: a single universal model applying fixed safety gu...

KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability

Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In cl...

MGM2 as a Unified Foundation Model for Microbiome World Exploration

Microbiomes are information-rich biological systems, yet most computational analyses still reduce communities to cohort-specific abundance tables. Her...

Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs

Attention and saliency heatmaps are widely used to explain medical Vision-Language Model (VLM) outputs on chest X-rays, yet whether they truly highlig...

Death in People with Down syndrome: Mortality statistics and novel predictors in US Medicaid and Medicare enrolled adults.

People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and dev...

SemVac: A Semantic Vaccinology Paradigm Powered by LLMs for Antigen Discovery

Reverse vaccinology has enabled sequence-based antigen discovery, but it overlooks the rich semantic knowledge embedded in the biomedical literature. ...

Parameter-efficient deep learning for pneumonia detection on chest X-rays: A comparative evaluation of explainable AI methods

Pneumonia is a leading cause of infectious disease mortality worldwide, accounting for approximately 2.5 million deaths annually and 15% of deaths in ...

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensiti...

SpaceBio Knowledge Hub: A LiteratOmics Platform for Microgravity and Space Biology Research

Space biology literature is growing exponentially. Existing infrastructure has not kept pace with organizing, synthesizing, and disseminating this kno...

From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models

Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screen...

Integrating planetary health and environmental justice into high school construction career education: protocol for a randomized controlled trial of the Ecosystem Justice Translator

Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnecte...

From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand

Chest radiography (CXR) remains the most widely used thoracic imaging modality, yet expert interpretation is constrained by a severe shortage of radio...

Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks

Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that th...

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly whi...

Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation

Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs...