Latest AI and machine learning research in medical education for healthcare professionals.
Chronic idiopathic constipation carries a significant burden on children and their families. Despite existing treatment guidelines, adherence and outcomes remain poor, with gaps in parental understanding contributing to these outcomes. This study aimed to develop a "curriculum of information needs" for parents of children with chronic constipation. This is a qualitative study which employed 15 sem...
PURPOSE: To compare the accuracy, readability and patient-centeredness of responses generated by standard ChatGPT-4o and its retrieval-augmented 'deep research' mode for hip arthroscopy education, addressing the current uncertainty about the reliability of large language models in orthopaedic patient information. METHODS: Thirty standardised patient questions were derived through structured search...
Visual function is one of the most critical abilities of organisms to perceive the outside world, playing an indispensable role in the interaction bet...
The Accreditation Council for Graduate Medical Education (ACGME) recently conducted their 10-year specialty-specific revision of the colon and rectal ...
Graph Neural Networks (GNNs) have become a powerful tool for modeling complex graph-structured data, achieving remarkable success in various graph min...
The rapid development of large language models (LLMs) has accelerated research into applying artificial intelligence (AI) to domains such as medical q...
INTRODUCTION: The rapid evolution of artificial intelligence (AI) is reshaping pharmacy continuing education (CE), offering innovative content generat...
BACKGROUND: Pre-registration nursing students need to be appropriately prepared for a healthcare environment that is increasingly utilising artificial...
OBJECTIVES: The efficacy of an MRI-based deep learning algorithm (DLA) for detecting acute ischemic stroke (AIS) was evaluated across readers with div...
In the rapidly advancing landscape of surgical education, the traditional apprenticeship model is being increasingly complemented by individualized le...
RATIONALE AND OBJECTIVES: Large language models (LLMs) are increasingly investigated in radiology education. This study evaluated the performance of s...
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, blending two or more images to generate strong-pertur...
INTRODUCTION AND OBJECTIVES: An AI model that performs well during training does not guarantee similar performance in clinical practice and should be ...
BACKGROUND: Intensive Care Unit (ICU) nursing is demanding, requiring advanced clinical decision-making and emergency management skills. Simulation-ba...
The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utili...
AIMS: The study focused on nurses' familiarity with, beliefs about, and attitudes towards artificial intelligence, aiming to identify configurations o...
Transcranial ultrasound imaging plays an important role in the diagnosis of brain diseases and the monitoring of brain function. However, the quality ...
The shortage of clinical training sites and preceptors has become a major barrier in the development of health workforce across North America. Innovat...
PURPOSE: To develop a 3D multiple overlapping-echo detachment (3D-MOLED) imaging technique, along with data generation and reconstruction strategies, ...
CLINICAL RELEVANCE: Communication between eye care practitioners is essential to optimise health care. Traditionally, actors have been used prior to r...