Latest AI and machine learning research in risk management for healthcare professionals.
Large language models are rapidly transitioning from pilot schemes to routine clinical practice. This creates an urgent need for clinicians to develop the necessary skills to strike the right balance between seizing opportunities and taking accountability. We propose a 3-tier competency framework to support clinicians' evolution from cautious users to responsible stewards of artificial intelligenc...
PURPOSE: To develop and evaluate short-TR acquisition time-of-flight (STRA-TOF) MR angiography (MRA), which combines an optimized STRA with deep learning-based reconstruction to achieve scan-time reduction while maintaining image quality in the visualization of intracranial arteries. METHODS: Ten healthy volunteers and 3 patients with moyamoya disease were examined using 3D TOF MRA with the clinic...
PURPOSE: To synthesize evidence from systematic reviews on artificial intelligence (AI) applications in prosthodontics and implant dentistry, focusing...
BACKGROUND: AI-enabled personalized treatment planning may improve outcomes by tailoring care, yet its clinical impact across modalities remains uncer...
INTRODUCTION: This study was conducted to explore the views of pediatric nurses on robot nurses and artificial intelligence. METHOD: A qualitative res...
UNLABELLED: Traditional clinical trial designs such as the isolated two-arm randomized controlled trial (RCT) do not offer robust solutions for evalua...
Artificial intelligence (AI) is rapidly transforming surgical care, offering unprecedented capabilities in diagnostics, planning, and intraoperative g...
High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by h...
OBJECTIVES: Amyloid-lowering immunotherapies can cause amyloid-related imaging abnormalities (ARIA), requiring brain MRI for detection and monitoring....
Compared with left heart catheterization (LHC), the pressure gradient of an aortic valve (PGAV) measured by echocardiography during transcatheter aort...
BACKGROUND: Accurate preoperative evaluation of rectal cancer is essential for staging and treatment planning. Low-energy virtual monoenergetic imagin...
Schizophrenia being a major psychiatric disorder comprises of dominant neurodevelopmental corroborations; still there are inadequate markers which rev...
Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the additio...
PURPOSE: To develop and validate a neural network-based Kid's Listening Performance Checklist (KLiP) for early identification of listening difficultie...
BACKGROUND: Radiology faces an unprecedented workload crisis, creating demand for AI solutions to enhance efficiency and quality. Vision-language mode...
INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobia...
OBJECTIVES: To systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in ra...
OBJECTIVES: The purpose of this study was to determine the utility of conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (D...
BACKGROUND: Asthma is the most common chronic disease in children. Suboptimal asthma control is prevalent and causes significant health care costs. El...
Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance be...