Cultural Competence

Multimodal brain-gut-sleep phenotypes predict delirium, long-term cognitive decline, and survival after colorectal cancer surgery.

BACKGROUND: Neuroimmune, circadian, autonomic, and gut-brain processes jointly shape vulnerability to postoperative delirium and long-term cognitive decline, yet their integrated contribution remains unclear. METHODS: In this prospective cohort (n = 300), preoperative assessments included circadian actigraphy, gut microbial diversity and short-chain fatty acids, inflammatory cytokines (IL-6, CRP),...

Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.

BACKGROUND: There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models remain uncertain. OBJECTIVE: This systematic review aims to evaluate the predictive performance and methodological quality of machine learning models for venous thromboembolism risk after joint replacement surgery. ME...

Neuroimaging in Low- to Middle-Income Countries: A Health Equity Perspective.

BACKGROUND: Inequities in neuroimaging access represent a major barrier to timely diagnosis and treatment of neurologic disease, particularly in low- ...

Expert Consensus on Ethical Governance for Clinical Applications of Generative Medical Artificial Intelligence(2025 Edition).

The research and innovative applications of generative medical artificial intelligence(GMAI)are rapidly advancing in the healthcare field.Significant ...

Evaluation of Large Language Models for Peer Review in Transplantation Research: Algorithm Validation Study.

BACKGROUND: Peer review remains central to ensuring research quality, yet it is constrained by reviewer fatigue and human bias. The rapid rise in scie...

Trainable-parameter-free structural-diversity message passing for graph neural networks.

Graph Neural Networks (GNNs) have achieved strong performance in structured data modeling such as node classification. However, real-world graphs ofte...

Learning dynamic binary treatment policies under treatment selection bias: A Conservative Q-Learning approach with representation balancing.

OBJECTIVE: Learn safe, robust dynamic treatment regimes (DTRs) from observational trajectories that exhibit treatment selection bias, using an offline...

Artificial Intelligence for Gastroenterology Practice: A Modified Delphi Consensus.

BACKGROUND: The American College of Gastroenterology (ACG) assembled a multidisciplinary task force to evaluate the current state and future direction...

Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms.

OBJECTIVES: Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies prov...

A Multiobjective Evolutionary Algorithm Based on Bipopulation With Uniform Sampling for Neural Architecture Search.

Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-p...

HBID24K: A New Benchmark Dataset for Vulnerable Houbara Bustard and Intruder Detection in Wildlife Monitoring.

Monitoring vulnerable Houbara bustards, birds of both ecological and cultural significance, and detecting intruders that can pose a threat to their ne...

Use of uterine activity to predict preterm birth by artificial intelligence assisted models: a narrative systematic review.

OBJECTIVE: The benefit of interventions to improve neonatal outcomes of preterm birth (PTB) must be balanced with the associated fetal and maternal ri...

Learning fair representation for fine-tuning pre-trained language models.

Pre-trained language models (PLMs) have achieved remarkable success across a wide range of natural language processing tasks, including text classific...

Sparsity and memory constraints interact with training sequence to bias learning of associative maps.

Cognitive maps support inference and planning by representing associations between experiences encoded in memory. These map-like representations are t...

Evaluation of an automated sleep apnea scoring algorithm via the Wesper Lab home sleep apnea test.

This study evaluated the performance of the Wesper Lab home sleep apnea test (HSAT) artificial intelligence (AI) automated scoring algorithm under bot...

Inferring context-specific site variation with evotuned protein language models.

Multiple sequence alignments (MSAs) have been traditionally used for making inferences about site-specific diversity in proteins. Recent advancements ...

Enhancing Arabic healthcare fake news detection with data augmentation and multi-metric analysis using large language models.

The spread of fake news about healthcare can result in a global health crisis, as it is easy to mislead the public. Detection of fake Arabic news in t...

Meta-analysis of the uncultured gut microbiome across 11,115 global metagenomes reveals a candidate signature of health.

The human gut microbiome is important for host health, yet over 60% of gut species remain uncultured and inaccessible to experimental manipulation. He...

Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.

BACKGROUND AND PURPOSE: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpin...

Synthetic health data in Canada: A scoping review of methods, applications, and data sources.

BACKGROUND: Access to provincial health-related data for multi-jurisdictional studies in Canada is restricted by privacy laws. Synthetic data (SD), wh...