Latest AI and machine learning research in risk management for healthcare professionals.
BACKGROUND: Developing high-quality multiple-choice examinations in medical education is time- and resource-intensive. Large language models (LLMs) offer a promising approach to accelerate question development; however, their utility for exam development remains underexplored. METHODS: The trial was a participant-blinded, parallel-group randomized controlled trial conducted among first-year medica...
INTRODUCTION: Conventional risk scores like EuroSCORE II and Society of Thoracic Surgeons models, derived from logistic regression, may not fully represent complex interactions within cardiac surgery cohorts. Using nonlinear modeling, machine learning (ML) may improve risk prediction by capturing complex relationships. OBJECTIVE: To conduct a systematic review of studies (2020-2026) that compare M...
INTRODUCTION: Contrast-enhanced CT is central to oncological imaging, yet no official guidelines exist for contrast injection protocols. As a result, ...
OBJECTIVES: To compare the acquisition time, image quality, and diagnostic confidence of DL-accelerated DIR (DIR-DL) with conventional MRI in patients...
OBJECTIVE: To evaluate resident versus attending operative notes using a two-phase approach combining natural language processing (NLP) diffing and st...
INTRODUCTION: The integration of artificial intelligence (AI) into healthcare is transforming nursing practice, introducing both opportunities and cha...
Predicting nucleophilicity and electrophilicity at atomic sites in organic compounds is crucial for the design of polar reactions. Methyl cation affin...
The objective was to evaluate the image quality and hepatic lesion conspicuity in a dual-low-dose (radiation and contrast volume) upper abdominal dual...
BACKGROUND: As the histopathology workforce continues to struggle and service demand continues to increase, it has become prudent to consider viable a...
INTRODUCTION: Sepsis is a life-threatening condition in intensive care units (ICUs), where any delay in diagnosis and treatment can lead to organ dysf...
Accurate demand forecasting for spare parts under true cold-start conditions remains a fundamental challenge due to extreme demand sparsity, zero infl...
BACKGROUND: Early-phase oncology trials involve complex protocols and extensive documents, making timely resolution of study queries challenging. We d...
There has been mounting pressure on cancer multidisciplinary team (MDT) meetings due to increasing case volumes. Efforts to streamline MDT workflows o...
Surgical patient safety remains a major challenge in resource-limited settings, where preventable harm may be increased by delayed assessment, limited...
Large language models (LLMs) are starting to be coupled with brain-computer interfaces (BCIs) for assistive communication, but the resulting systems d...
BACKGROUND: Artificial intelligence (AI)-based nursing interventions are increasingly being employed to manage chronic illnesses; however, their defin...
BACKGROUND: Developmental dysplasia of the hip (DDH) is among the most common musculoskeletal disorders in infants. Ultrasound, especially the Graf me...
BACKGROUND AND OBJECTIVES: Health economic modelling integrates evidence from multiple sources and relies on transparency to support reimbursement dec...
BACKGROUND: Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide. Retinal imaging and deep learning (DL) may ...
BACKGROUND: Novice health care staff often write case reports during early clinical training. However, many institutions lack structured feedback syst...