Information Technology

Characteristics and Outcomes of Over One Million Veterans with Heart Failure Phenotyped Using Artificial Intelligence Approaches: The National DCVA-HF Registry.

BACKGROUND: Major heart failure (HF) registries are limited by manual chart abstraction and to hospitalized patients. Using artificial intelligence (AI) approaches and electronic health record (EHR) data from the Veterans Affairs (VA) healthcare system, we have assembled the National DCVA-HF Registry, which includes both ambulatory and hospitalized HF patients. In the current study, we descriptive...

Responsible AI Integration Framework for Psychiatric Guidelines.

Artificial intelligence (AI) is reshaping medicine, promising advances in diagnosis, monitoring, and treatment, and psychiatry will be no exception. Yet the field remains fragmented: ethical guidelines, technical standards, and clinical workflows have evolved in parallel, creating uncertainty about how to integrate AI safely and meaningfully into psychiatric care. Existing frameworks often address...

Leveraging routine clinical data for dementia risk prediction using machine learning.

BackgroundEarly diagnosis of dementia is essential for enabling timely interventions that may slow disease progression, improve patient outcomes, and ...

Using Large Language Models to Identify Patient-Oncologist Communication Domains: A Feasibility Study.

BACKGROUND: The American Society of Clinical Oncology (ASCO) convened a multidisciplinary panel in 2017, resulting in patient-oncologist communication...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

[Digital twins in uro-oncology].

BACKGROUND: Uro-oncology is moving toward precision medicine, driven by high-dimensional longitudinal data from imaging, pathology, molecular profilin...

Dataset for integrity attacks on time synchronized synchrophasor data.

Phasor measurement units, also known as synchrophasors, are a vital component within smart grids to determine the stability of the grid. These devices...

Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.

OBJECTIVES: Chronic venous disease (CVD) is one of the most common vascular disorders, affecting millions of people worldwide. Owing to the variabilit...

Integrating automation into construction waste quantification: A review of digital innovations for sustainable resource management.

The construction sector is a major generator of solid waste, with construction and demolition waste (CDW) posing significant challenges to sustainable...

Challenges and Barriers in Implementing AI for Clinical Applications in Anatomic Pathology.

The transformation of anatomic pathology from microscope-based practice to digital and computational workflows has created unprecedented opportunities...

Technology in Diabetes: A Year in Review.

The past year has continued the rapid evolution of diabetes technology across the monitoring, delivery, analytics, and patient-support domains. Improv...

Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.

BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is integral to preoperative risk assessment; yet, assign...

Artificial intelligence assisted structural realignment of high-rise buildings through lifting, grouting and reinforcement.

The use of artificial intelligence (AI) in high-rise building rectification is emerging as an essential way to address structural issues. This study e...

HRS scientific statement on artificial intelligence integration framework into clinical electrophysiology workflows.

Artificial intelligence (AI) and digital health technologies (DHTs) are rapidly transforming health care, offering unprecedented opportunities for enh...

From prediction to practice: early implementation of a machine learning-based hospitalization prediction tool in the emergency department.

BACKGROUND: Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains ra...

Artificial Intelligence In The Diagnosis And Prediction Of Breast Cancer-Related Lymphedema: A Scoping Review.

INTRODUCTION: Lymphedema is a chronic complication of breast cancer treatments that can significantly impact the well-being of survivors. This scoping...

Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.

BACKGROUND: Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-w...

Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine Learning.

In this secondary analysis of a German cross-sectional survey data, we investigated key determinants and predictors of telemedicine (TM) use among hea...

A cross-dataset harmonized intrusion detection framework with statistically validated multi-model learning.

Intrusion Detection Systems (IDS) are considered critical security tools in ensuring network infrastructure security. However, recent studies on machi...

Addressing diagnostic code variability in intimate partner violence surveillance through natural language processing: Evidence from substance use disorder populations.

BACKGROUND: Intimate partner violence (IPV) and substance use disorders (SUDs) represent major public health challenges, yet identifying IPV cases in ...