Emergency Medicine

On the Feasibility of Poisoning Text-to-Image AI Models via Adversarial Mislabeling

Today's text-to-image generative models are trained on millions of images sourced from the Internet, each paired with a detailed caption produced by Vision-Language Models (VLMs). This part of the training pipeline is critical for supplying the models with large volumes of high-quality image-caption pairs during training. However, recent work suggests that VLMs are vulnerable to stealthy adversa...

A Comprehensive Dataset for Underground Miner Detection in Diverse Scenario

Underground mining operations face significant safety challenges that make emergency response capabilities crucial. While robots have shown promise in assisting with search and rescue operations, their effectiveness depends on reliable miner detection capabilities. Deep learning algorithms offer potential solutions for automated miner detection, but require comprehensive training datasets, which...

Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration

Osteoporosis, characterized by reduced bone mineral density (BMD) and compromised bone microstructure, increases fracture risk in aging populations....

Automatic Cannulation of Femoral Vessels in a Porcine Shock Model

Rapid and reliable vascular access is critical in trauma and critical care. Central vascular catheterization enables high-volume resuscitation, hemo...

A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.

The revisit of the emergency department (ED) is a key indicator of emergency care quality. Various strategies have been proposed to reduce ED revisits...

Semantic Scheduling for LLM Inference

Conventional operating system scheduling algorithms are largely content-ignorant, making decisions based on factors such as latency or fairness with...

Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models

Interpreting large volumes of high-dimensional, unlabeled data in a manner that is comprehensible to humans remains a significant challenge across v...

Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?

This paper introduces the TempVS benchmark, which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models (MLLM...

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation

Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large...

Knowledge Graph-Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation.

BACKGROUND: Although much progress has been made in artificial intelligence (AI), several challenges remain substantial obstacles to the development a...

Primer on large language models: an educational overview for intensivists.

The integration of artificial intelligence (AI) and machine learning-enabled medical technologies into clinical practice is expanding at an unpreceden...

Artificial intelligence and endoanal ultrasound: pioneering automated differentiation of benign anal and sphincter lesions.

BACKGROUND: Anal injuries, such as lacerations and fissures, are challenging to diagnose because of their anatomical complexity. Endoanal ultrasound (...

Generative Voice Bursts during Phone Call

In critical situations, conventional mobile telephony fails to convey emergency voice messages to a callee already engaged in another call. The stan...

C-PATH: Conversational Patient Assistance and Triage in Healthcare System

Navigating healthcare systems can be complex and overwhelming, creating barriers for patients seeking timely and appropriate medical attention. In t...

A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization

Patients have distinct information needs about their hospitalization that can be addressed using clinical evidence from electronic health records (E...

Conformal coronary calcification volume estimation with conditional coverage via histogram clustering

Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. H...

Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.

Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This...

A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation

Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors ...

Aryl Organophosphate Esters and Hemostatic Disruption: Identifying Risk through Machine Learning and Experimental Validation.

Organophosphate esters (OPEs) have emerged as a significant environmental concern due to their widespread occurrence and potential human health risks....

Dirty and Clean-Label attack detection using GAN discriminators

Gathering enough images to train a deep computer vision model is a constant challenge. Unfortunately, collecting images from unknown sources can lea...