Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.
OBJECTIVES: To evaluate whether collaborative assistance from an artificial intelligence-based tool that proposes partial radiology report content can improve reporting efficiency and radiologist satisfaction in chest X-ray interpretation, without compromising report quality. MATERIALS AND METHODS: In a retrospective study, three radiologists reported 50 MIMIC-CXR chest X-rays twice, once with art...
BACKGROUND: Artificial intelligence (AI) is expanding across various medical fields, with machine learning (ML) being increasingly used to enhance patient management in diagnosis, prevention, and therapeutic care. OBJECTIVE: This study aims to provide an overview of ML applications in HIV care, focusing on real clinical data to improve health care for people living with HIV and on antiretroviral t...
BACKGROUND: Social determinants of health (SDOH) are the social, economic, and environmental conditions that influence health outcomes. SDOH informati...
OBJECTIVES: Accurate prediction models for imported infectious diseases are essential for early warning, cross-border surveillance, and resource alloc...
Ammonia (NH3) is a promising carbon-free energy carrier, and its synthesis is a key process in the chemical industry. While the Haber-Bosch process re...
OBJECTIVE: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for pr...
BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet the...
This scoping review aimed to answer the question: to what extent do artificial intelligence applications in dental and orthopedic skeletal imaging dem...
Reproducibility of machine learning applications in clinical informatics heavily relies on data preparation. However, preprocessing pipelines are not ...
OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitativ...
INTRODUCTION: The growing volume of primary research and the increasing demand for timely, high-quality evidence syntheses (ESs) have intensified inte...
AIM: To explore nurses' lived experiences of a generative artificial intelligence-enabled shift handover innovation. DESIGN: A descriptive phenomenolo...
BACKGROUND: Artificial intelligence-based radiomics offers a potential adjunct to the current clinical management of paediatric brain tumours by enabl...
RATIONALE AND OBJECTIVES: Timely radiology access is essential for accurate diagnosis, treatment planning, and efficient care delivery. U.S. academic ...
BACKGROUND: Deep learning (DL)-based artificial intelligence (AI) models, the fourth generation in autosegmentation, have been adopted both for commer...
BACKGROUND: Accurate assessment of burn depth and area are required to guide treatment and inform prognosis. Currently this assessment relies on subje...
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have increasingly transformative potential in orthopedic surgery, enhancing precisi...
Chest radiography is the most frequently performed imaging examination worldwide, and increasing demand has contributed to reporting delays in many he...
OBJECTIVES: Coronary computed tomography angiography (CCTA) has become a cornerstone in non-invasive CAD diagnosis and risk stratification. To standar...
Algorithms that support screening, triage, and treatment decisions depend on training data drawn from patient populations. Limited access to patient-l...