Information Technology

A novel intrusion detection system for IIoT in 5G networks using attention-augmented federated learning and lightweight transformer architectures.

The swift proliferation of Industrial Internet of Things (IIoT) devices within 5G networks has intensified cybersecurity vulnerabilities owing to their limited processing capabilities and fluctuating network conditions. This work presents an innovative intrusion detection system (IDS) designed for IIoT within 5G environments, overcoming the shortcomings of conventional hybrid deep learning models....

Enhancing healthcare information security through VAE-driven anomaly detection in EHR access patterns.

HIPAA breaches and unauthorized access to Electronic Health Records (EHRs) have been growing more likely due to the sudden digitalization of the healthcare sector. High endurance, privacy-based security practices have never been more in demand as hospitals and other medical facilities of this type have clung to the electronic system. The given study considers this problem by suggesting an anomaly ...

Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.

This review aims to critically evaluate sustainable smart sensing technologies and AI-driven platforms for real-time mycotoxin detection, highlighting...

i2b2-ML: module to facilitate machine learning in the informatics for integrating biology and the bedside platform.

OBJECTIVE: Although machine learning (ML) holds significant potential to transform healthcare, there has been a recent surge in research output that o...

Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study.

BACKGROUND: Protein-energy wasting (PEW) is common in chronic kidney disease (CKD) and is linked to poor outcomes. Early risk stratification may enabl...

Methods for determining indications for antibiotic prescriptions to facilitate antimicrobial stewardship: a scoping review.

BACKGROUND: Effective antimicrobial stewardship (AMS) requires accurate information on the reason for prescribing antimicrobials. To design targeted i...

[Clinical practice guidelines for the application of intelligent technology in spinal surgery (2026 edition)].

In response to the challenges of insufficient precision and limited safety associated with traditional techniques in the diagnosis and treatment of co...

Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study.

BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding ope...

Biobanking in the Era of Artificial Intelligence: Convergence, Challenges, and Opportunities.

Artificial intelligence (AI) is advancing rapidly, transforming biomedical research and health care through software applications ranging from diagnos...

Advances in root phenotyping: high-throughput imaging, computational tools and integrative approaches for crop improvement.

Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper ...

Parallel AI-driven framework for post-quantum secure medical image communication using swin-transformer restoration.

Reliable and secure transmission of medical images is essential for telemedicine, remote diagnosis, and distributed healthcare systems. However, medic...

From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.

BACKGROUND: Electronic health records (EHRs) are central to clinical documentation and care coordination, yet long-standing studies report persistent ...

Vision-language models as an integrative layer for clinical artificial intelligence in radiology: A systems-level perspective.

Clinical adoption of artificial intelligence (AI) in radiology has matured through task-specific tools for detection, segmentation, triage, and quanti...

Advancing stroke prevention in atrial fibrillation: a systematic review of machine learning-based risk prediction models.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and confers a four to fivefold increase in ischemic stroke risk, ...

Integrating AI into hospital workflows: implementation strategies and real-world barriers.

Artificial intelligence (AI) is increasingly being integrated into hospital systems with the potential to transform clinical workflows, operational ef...

Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.

BACKGROUND: Major depressive disorder (MDD) and treatment-resistant depression (TRD) are heterogeneous conditions in which key clinical details are sp...

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often unde...

LOINC-Related Clinical Terminology Management in an Academic Medical Center Using Human in the Loop Machine Learning.

OBJECTIVE: Many institutions have reported poor mapping of laboratory results to Logical Observation Identifiers Names and Codes (LOINC). A human in t...

Beyond the digital health tsunami: reaffirming biomedical and health informatics as the scientific foundation of digital healthcare transformation.

Digital health (DH) has emerged as the dominant terminology for technology-enabled healthcare, reshaping institutional discourse, educational programm...

Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.

Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and ...