Information Technology

Artificial intelligence in geriatric healthcare: a scoping review.

BACKGROUND: Deep demographic ageing is triggering a twin crisis in healthcare: a rising prevalence of multimorbidity among older adults and a severe global shortage of healthcare professionals, particularly nursing staff, which together create life-threatening gaps in long-term care. Although artificial intelligence shows significant potential to alleviate these pressures by improving the efficien...

The Open Syndrome Definition as a Machine-Readable Standard for Public Health: Design and Implementation Study.

BACKGROUND: Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizati...

Optical communication network signal analysis and cyber security modelling by frequency modulation with machine learning algorithm.

Machine learning (ML) methods are commonly used in optical communication networks to enhance bandwidth analysis through efficient resource management ...

Effect of an electronic health record-integrated machine learning asthma risk marker on pediatrician prognostic accuracy during preschool age: a pilot randomized clinical trial.

Early identification of children at risk for persistent asthma is challenging because preschool respiratory symptoms are heterogeneous and often overl...

AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.

Artificial intelligence (AI) is transforming synthetic chemistry from task-specific predictors into integrated platforms that unify retrosynthesis, re...

From development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis.

Recent research has increasingly focused on machine learning (ML) models for early disease prediction, yet practical frameworks for integrating these ...

Towards autonomous medical artificial intelligence agents.

Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather t...

The role of artificial intelligence in predicting COPD exacerbations using multimodal data: a systematic review.

BACKGROUND: COPD remains a leading cause of global morbidity and mortality, with acute exacerbations driving disease progression and healthcare utilis...

Multimodal Deep Learning for Predicting Deep Vein Thrombosis Risk After Total Knee Arthroplasty: A Clinical Study.

BACKGROUND: Deep vein thrombosis (DVT) following total knee arthroplasty (TKA) remains a major postoperative complication. Conventional risk assessmen...

Digital Dermatology.

Digital transformation is fundamentally reshaping dermatology, creating new opportunities in diagnostics, therapy, and healthcare organization. Large ...

Operationalizing the Pan-Canadian Health Data Charter Through Common Standards and AI-Enabled Nursing Data.

Principle 4 of the Pan-Canadian Health Data Charter calls for common standards to enable interoperability, access, and portability of health data. Yet...

Implementing Digital Respiratory Technologies for People With Respiratory Conditions: Scoping Review.

BACKGROUND: Digital health offers opportunities for safe, equitable, and accessible care, and its integration into respiratory care is a strategic pri...

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nont...

Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.

The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically chal...

Artificial Intelligence in Demand and Capacity Modelling of Healthcare Systems.

The increasing complexity of healthcare systems management requires the development of advanced methodologies to support efficient resource allocation...

Knowledge-Augmented Large Language Model for Multimodal Electronic Health Record-Based Risk Prediction: Development and Validation Study.

BACKGROUND: Accurate clinical outcome prediction using electronic health records (EHRs) is crucial for patient care and resource allocation. EHRs incl...

Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study.

BACKGROUND: Telehealth expansion and artificial intelligence (AI) adoption are often described as parallel dimensions of health system digital transfo...

Artificial intelligence-powered cloud security strategies for protecting critical clinical operations in healthcare environments.

The increasing use of cloud computing in hospitals, telemedicine, the Internet of Medical Things (IoMT) and real-time patient monitoring has made for ...

Regulatory science for AI-based software as a medical device in computational pathology and biomarker-driven drug development.

BACKGROUND: Artificial intelligence (AI)-enabled software is increasingly integrated into digital and computational pathology, driving new regulatory ...

AIDx: a locally deployable AI system for physician clinical decision support.

The dynamic environment of medicine, particularly in settings such as the Emergency Department, challenges physicians with an influx of patient data a...