Latest AI and machine learning research in intensivists for healthcare professionals.
While previous multimodal slow-thinking methods have demonstrated remarkable success in single-ima...
Septic acute respiratory distress syndrome (ARDS) is a complex and noteworthy type, but its molecula...
Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse d...
Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strate...
In the early stages of architectural design, shoebox models are typically used as a simplified rep...
The complementary information found in different modalities of patient data can aid in more accurate...
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of feat...
The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This s...
Deploying depth estimation networks in the real world requires high-level robustness against vario...
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical st...
The complex mechanical environment of peripheral arteries makes stents with poor torsional performan...
Landslides are among the most common natural disasters globally, posing significant threats to hum...
The multivariate, asynchronous nature of real-world clinical data, such as that generated in Inten...
Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., mu...
Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a nec...
This paper introduces Multi-Modal Retrieval-Augmented Generation (M^2RAG), a benchmark designed to...
Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are ...
Heart failure is one of the leading causes of death worldwide, with millons of deaths each year, a...
In critical care settings, where precise and timely interventions are crucial for health outcomes,...
Multi-modal Large Language Models (MLLMs) are capable of precisely extracting high-level semantic ...
Predicting medical events in advance within critical care settings is paramount for patient outcom...