Latest AI and machine learning research in critical care for healthcare professionals.
Recognition of respiratory distress through visual inspection is a life saving clinical skill. Clinicians can detect early signs of respiratory deterioration, creating a valuable window for earlier intervention. In this study, we evaluate whether recent advances in video transformers can enable Artificial Intelligence systems to recognize the signs of respiratory distress from video. We collected ...
Accurate clinical triage is critical for optimizing decision-making and resource allocation during infectious disease outbreaks such as COVID-19. In this study, we present an AI-driven decision-support tool for the triage of COVID-19 patients based on respiratory microbiome profiles derived from shotgun metagenomic sequencing. We analyzed 477 shotgun respiratory metagenomes from three independent ...
The basic computational unit of the brain has long been defined as the neuron. However, mounting evidence suggests that other cells, especially astroc...
Composed Image Retrieval (CIR) aims to retrieve target images based on a reference image and modified texts. However, existing methods often struggle ...
Background: Diffusion MRI (dMRI) is widely used to assess microstructural abnormalities in multiple sclerosis (MS), yet conventional diffusion tensor ...
Objective: To evaluate a ranking approach for emergency department (ED) waiting room prioritization that uses pairwise clinical comparisons aggregated...
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a l...
As multi-agent AI systems are increasingly deployed in real-world settings - from automated customer support to DevOps remediation - failures become h...
Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition...
State-of-the-art text-to-image models produce high-quality images, but inference remains expensive as generation requires several sequential ODE or de...
While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether th...
With the rapid advancement of artificial intelligence, intelligent dentistry for clinical diagnosis and treatment has become increasingly promising. A...
Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware appro...
Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventila...
Recent advances in Large Language Models (LLMs) present new opportunities for automating critical bottlenecks in scientific workflows such as literatu...
Brief Hospital Course (BHC) narratives must be clinically useful yet faithful to fragmented EHR evidence. LLM-based clinical summarizers still introdu...
Hospital artificial intelligence (AI) and robotics are spreading unevenly across the United States, yet national evidence on how these technologies ar...
Abstract Background: Echocardiography (echo) notes contain valuable prognostic information for patients in the intensive care unit (ICU). However, the...
Background: Pleuroparenchymal fibroelastosis (PPFE) is an upper lobe predominant fibrotic lung abnormality associated with increased mortality in esta...
Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reasoning for nodule delineation and local, texture-dri...