Pneumonia

ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion

Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision--language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-s...

XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis

Chest X-ray (CXR) interpretation is a fundamental yet complex clinical task that increasingly relies on artificial intelligence for automation. However, traditional monolithic models often lack the nuanced reasoning required for trustworthy diagnosis, frequently leading to logical inconsistencies and diagnostic hallucinations. While multi-agent systems offer a potential solution by simulating coll...

Data Diversity vs. Model Complexity in the Prediction of Pediatric Bipolar Disorder: Evidence from Academic and Community Clinical Samples

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...

RVLM: Recursive Vision-Language Models with Adaptive Depth

Medical AI systems face two fundamental limitations. First, conventional vision-language models (VLMs) perform single-pass inference, yielding black-b...

AI-Assisted Pneumonia Detection, Localisation and Report Generation from Chest X-rays

Purpose: Pneumonia detection in chest X-rays (CXRs) is complicated by high inter-observer variability and overlapping radiographic patterns. While dee...

Consistent but Dangerous: Per-Sample Safety Classification Reveals False Reliability in Medical Vision-Language Models

Consistency under paraphrase, the property that semantically equivalent prompts yield identical predictions, is increasingly used as a proxy for relia...

LoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray

Fine-grained representation learning is crucial for retrieval and phrase grounding in chest X-rays, where clinically relevant findings are often spati...

Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following

Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visua...

Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search

Automated radiology report generation has gained increasing attention with the rise of deep learning and large language models. However, fully generat...

CompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation

Generative models are increasingly used to augment medical imaging datasets for fairer AI. Yet a key assumption often goes unexamined: that generators...

A Retrospective Multi-Source Clinical Validation of Lenek Intelligent Radiology Assistant: An Artificial Intelligence-Based Chest Radiograph Screening and Triage System for High-Burden Pulmonary and Cardiac Conditions in India

Background: A critical radiologist shortage exists in India, leading to delayed chest radiograph (CXR) interpretation. This leads to disease progressi...

Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models

The clinical deployment of AI diagnostic models demands more than benchmark accuracy - it demands robustness across the full spectrum of disease prese...

Eukaryotic MAGs recovered from deep metagenomic sequencing of the seagrass, Zostera marina, include a novel chytrid in the order Lobulomycetales

Fungi play pivotal roles in terrestrial ecosystems as decomposers, pathogens, and endophytes, yet their significance in marine environments is often u...

Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting

Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must make man...

Fine-scale environmental, genetic, and temporal factors can drive the coral, sediment, and water column metagenome on reefs

Understanding the stability of microbial community assembly on coral reefs is crucial for determining their response to changing environments. Here, w...

Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data

Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach for ensuring trustwo...

Improving the detection of clinically significant steatotic liver disease using a machine learning algorithm in a real-world primary care population

Background and aims Population screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due...

Leveraging publicly available datasets and machine learning approaches for predicting the health benefits of fermented foods

Fermented foods are an ancient, near universal component of human dietary culture and are increasingly recognized for their health benefits. Bioactive...

The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis

Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the po...

Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation

Radiologists highly desire fully automated AI for radiology report generation (R2G), yet existing approaches fall short in clinical utility. Reinforce...