Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model generalization in traditional Federated Learning (FL) due to high data heterogeneity. However, existing PFL methods often require increased communication rounds to achieve the desired performance, primarily due to slow train...
For more than 60 years, artificial intelligence (AI) has served as a mainstay in augmenting and assisting the lives of individuals across a wide array of interests and professional fields. Functioning to create deep computer simulations, analyze data, solve problems, and synthesize human behavior/emotion, AI has recently become a topic of popular interest in many fields of medicine. Despite decade...
During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online plat...
The growing adoption of large pre-trained models in edge computing has made deploying model inference on mobile clients both practical and popular. ...
With the rapid development of autonomous driving and extended reality, efficient transmission of point clouds (PCs) has become increasingly importan...
Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standard...
This paper introduces FedGenGMM, a novel one-shot federated learning approach for Gaussian Mixture Models (GMM) tailored for unsupervised learning s...
Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participant...
Deep joint source-channel coding (deepJSCC) and semantic communication have shown promising improvements in communication performance over wireless ...
BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulti...
Detecting lung abnormalities via chest X-rays is challenging due to understated tissue variations often ignored by traditional methods. Augmentation t...
The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or im...
PURPOSE: Automated Whole-Breast Ultrasound (ABUS) has been widely used as an important tool in breast cancer diagnosis due to the ability of this tech...
External and middle ear diseases are common disorders, especially in children, and can be examined using a digital otoscope. Hearing loss can result f...
Biosecurity practices are the cornerstone of disease prevention and control programs. In Canada, their implementation is evaluated with a Risk Assessm...
This systematic review evaluates the impact of the large language model (LLM) ChatGPT in oral and maxillofacial surgery. Following PRISMA guidelines a...
Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide...
Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals...
BACKGROUND: Effective communication skills are fundamental for health care professionals, yet conventional training methods face challenges in scalabi...
To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learni...