Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Background: Objective Structured Clinical Examination (OSCE; Clinical Performance Examination [CPX] in South Korea) is a high-stakes assessment of clinical performance, communication, and reasoning during time-limited patient encounters. As AI-enabled virtual standardized patient (VSP) simulation and automated scoring are introduced for OSCE-like training, prospective evidence is needed on how suc...
Purpose: To evaluate whether large language models (LLMs) can enhance clinician-patient communication by simplifying radiology reports to improve patient readability and comprehension. Methods: A randomised controlled trial was conducted at a single healthcare service for patients undergoing X-ray, ultrasound or computed tomography between May 2025 and June 2025. Participants were randomised in a ...
Federated learning (FL) facilitates the secure utilization of decentralized images, advancing applications in medical image recognition and autonomous...
Restoring communication for people with dysarthria secondary to pontine stroke remains a critical challenge. Intracortical brain-computer interfaces (...
Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, providing a sequential interface for transformer-ba...
Background and Aims: Alcohol use disorder (AUD) remains a major public health concern, with persistent disparities in access to evidence-based treatme...
Background: Australian health practitioners are regulated under the Health Practitioner Regulation National Law, with serious conduct matters referred...
Semantic communication is a novel communication paradigm that focuses on conveying the user's intended meaning rather than the bit-wise transmission o...
INTRODUCTION: Cognitively unimpaired (CU) adults show substantial variation in their risk of developing mild cognitive impairment (MCI), yet most subt...
Large-scale diffusion models such as FLUX (12B parameters) and Stable Diffusion 3 (8B parameters) require multi-GPU parallelism for efficient inferenc...
Scaling federated learning (FL) to billion-parameter models introduces critical trade-offs between communication efficiency, model accuracy, and priva...
Hospital-acquired infections (HAIs) significantly increase patient mortality and healthcare burden, with multidrug-resistant organisms (MDROs) exacerb...
Atypical Parkinsonian Disorders (APD), also known as Parkinson-plus syndrome, are a group of neurodegenerative diseases that include progressive supra...
Background: Cardiovascular diseases (CVDs) remain the leading global cause of morbidity and mortality. In clinical practice, 10-year risk prediction t...
Diffusion Transformers (DiTs) have gained increasing adoption in high-quality image and video generation. As demand for higher-resolution images and l...
Enabling natural communication through brain-computer interfaces (BCIs) remains one of the most profound challenges in neuroscience and neurotechnolog...
One-third of the world's 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting ...
We propose a drone signal out-of-distribution detection (OODD) algorithm based on the cognitive fusion of Zadoff-Chu (ZC) sequences and time-frequency...
In biomedical engineering, artificial intelligence has become a pivotal tool for enhancing medical diagnostics, particularly in medical image classifi...
Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management...