Cultural Competence

irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.

BACKGROUND: Large language models (LLMs) have emerged as transformative technologies, revolutionising natural language understanding and generation across various domains, including medicine. In this study, we investigated the capabilities, limitations, and generalisability of Generative Pre-trained Transformer (GPT) models in analysing unstructured patient notes from large healthcare datasets to ...

Feasibility and Acceptability of AI-Powered Tools for Early Autism Screening in Egypt: Semistructured Focus Group Study.

BACKGROUND: Autism spectrum disorder (ASD) is often underdiagnosed in low- and middle-income countries due to limited specialist access, sociocultural stigma, and fragmented screening systems. Artificial intelligence (AI)-powered screening tools may improve early detection by enabling low-cost, accessible assessments. However, adoption depends on stakeholder trust, ethical safeguards, and alignmen...

A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol.

INTRODUCTION: Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care provide...

Artificial intelligence in scholarly peer review: a scoping review of applications, risks, and governance challenges.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into scholarly publishing workflows, extending beyond manuscript preparation into ...

Technology Matters: Emerging technologies in clinical informatics to support child and adolescent mental healthcare safety.

Electronic health records (EHRs) can support patient safety across medical settings but require thoughtful adaptation to serve specialty care. This ar...

Screening and dynamic change study of microbial and metabolite markers for calf diarrhea based on multi-omics and machine learning.

UNLABELLED: Neonatal calf diarrhea is a leading cause of calf mortality and substantial economic loss in the livestock industry, yet the dynamic micro...

Investigating the factors of three-wheeled autorickshaw (3W-AR) crash severity: comparison of explainable machine learning models.

Three-wheeled autorickshaws (3W-ARs), locally called Bajaj, are a vital mode of public transport in Ethiopia. However, their crash involvement remains...

mHealth-Enabled Stroke Screening for Pediatric Sickle Cell Disease in Low-Resource Settings: Systematic Literature Review of Critical Barriers, Emerging Technologies, and AI-Driven Solutions.

BACKGROUND: Sickle cell disease (SCD) is a genetic blood disorder affecting millions globally, with life-threatening complications, and most patients ...

Digital health and machine learning in population health and wellness management: A scoping and bibliometric review of effectiveness, equity, and implementation challenges.

Machine learning (ML) in digital health applications is becoming more popular for the general management of population wellness and the promotion of l...

[Simulated consciousness, pseudo-empathy and the illusion of attachment: what can psychiatry do with artificial intelligence?].

Over the past decade, the burden of mental disorders has grown while services remain capacity-constrained, pushing generative artificial intelligence ...

Predictive models for ICU patient readmission based on machine learning: A systematic review.

BACKGROUND: Artificial intelligence (AI) prediction models can accurately identify high-risk populations by integrating multi-dimensional clinical dat...

Integrating Genomics, Collections, and Community Science to Delimit Species Clarifies the Taxonomy of a Variable Monitor Lizard (Varanus tristis).

The accurate characterization of species diversity is a vital prerequisite for ecological and evolutionary research, as well as conservation. Thus, it...

Underperformance of Machine Learning Models Predicting Readmission and Prolonged Length of Stay Following Total Knee Arthroplasty Among Underrepresented Populations: Fairness Analysis and Mitigation Strategies.

BACKGROUND: While machine learning (ML) models demonstrate high predictive accuracy, recent studies reveal that ML models underperform for smaller sub...

Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.

BACKGROUND: Rare diseases affect more than 300 million people globally, and only about 5% have approved therapies. Lysosomal storage disorders (LSDs) ...

Protein and genomic language models uncover the unexplored diversity of bacterial immunity.

The bacterial pangenome contains a vast diversity of antiphage systems, whose overall extent is still unknown. In this study, we developed complementa...

Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.

BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making i...

Follow-Up Bias in Tumor Dynamic Modeling: A Comparison of Classical and Neural-ODE Approaches.

Tumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously ass...

ChunkyBERT: a novel technique for multiclass political bias detection in news media.

With the increasing use of digital platforms for spread of information, political news has some of the most skewed sources which has confused people o...

Hypergraph Foundation Model.

Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by conn...

Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.

Adolescent mental health is foundational to personal development, yet it faces escalating challenges globally. While traditional assessment methods la...