Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models

Multi-modality medical vision (MV) foundation models (FM) are fundamentally challenged by pronounced...

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

Despite strong predictive results in the clinical machine learning literature, the translation of th...

MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems

Autonomous agentic systems are largely static after deployment: they do not learn from user interact...

Language-dependent diagnostic safety of medical AI systems: a cross-lingual benchmarking and prospective clinical study

Background Patients worldwide receive healthcare in many languages, yet medical AI systems are valid...

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentat...

Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow Efficiency

Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric as...

Fiber dispersion in the right ventricle: A comparison of constitutive neural network predictions with experimental data

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstr...

Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model

Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient ...

MechVerse: Evaluating Physical Motion Consistency in Video Generation Models

Text- and image-conditioned video generation models have achieved strong visual fidelity and tempora...

Improving machine learning and deep learning models for 30-day ICU readmission prediction using Ensemble Bayesian Model Averaging

Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased h...

Multi-Narrow Transformation as a Single-Model Ensemble: Boundary Conditions, Mechanisms, and Failure Modes

Single-model ensembles (SMEs) have attracted attention as a way to approximate some of the benefits ...

Focusable Monocular Depth Estimation

Monocular depth foundation models generalize well across scenes, yet they are typically optimized wi...

Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories

We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pret...

Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive ...

Outcome Prediction Models for Critically Ill Patients Using Small Routine Laboratory Datasets

We present a suite of foundational, outcome prediction models for critically ill patients, developed...

Generative Augmentation Reveals Previously Overlooked Signals in Transcriptomic Datasets

Identifying robust gene expression signatures from transcriptomic studies with small sample sizes re...

From Protocol to Practice: Graded Sepsis Bundle Compliance and Actionable Insights from Real-World ICU Data

Sepsis is a leading cause of in-hospital mortality, yet systematically evaluating temporal adherence...

Generalizing intensive care AI across time scales in resource-limited settings

Temporal resolution of physiological monitoring in intensive care varies widely across healthcare sy...

GeneBench: Assessing AI Agents for Multi-Stage Inference Problems in Genomics and Quantitative Biology

We introduce GeneBench, a benchmark for AI agents on realistic multi-stage scientific data analysis ...

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