Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data. In practice, however, each institution annotates only the pathologies within its area of expertise, so the federation operates under task heterogeneity: each client holds labels for a strict subset of the target disease categories while the remainin...
This paper introduces a method for real-time processing and transmission of autonomous underwater vehicle (AUV) imagery over low-bandwidth communication links. It leverages artificial intelligence (AI) techniques to identify a set of images that best represent an entire dataset, or automatically finds the most similar images to a given query image for transmission to operators. Combined with metad...
Real-time mobile 3D reconstruction is fundamental to many emerging applications such as autonomous navigation and digital twin construction, where a m...
Adolescent use of alcohol, nicotine, and marijuana remains a major public health concern in the United States. Early identification of youth at elevat...
Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnecte...
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so wit...
Background: Melanoma represents a highly immunogenic and therapeutically challenging malignancy. The complex cellular ecosystem of the tumor microenvi...
As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_...
Generative AI tools such as ChatGPT are increasingly used by the public to seek guidance on diet and physical activity for type 2 diabetes (T2D) preve...
Short-form video platforms increasingly shape how young audiences encounter health information. Generative artificial intelligence can produce standar...
Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These inc...
Background: Calcium oxalate nephrolithiasis is the most common type of kidney stone disease. Dietary oxalate intake is an important modifiable factor....
Multimodal Large Language Models (MLLMs) show great potential in medical tasks, but their elicited confidence often misaligns with actual accuracy, po...
Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and hig...
BackgroundAccess to high-quality clinical data is essential for advancing medical research and developing effective medical statistical and Artificial...
In primary care and outpatient settings, clinically important patient information is often embedded in fragmented, ambiguous, repetitive, and noisy co...
Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically design...
Advances in high-throughput neural recording technologies enable simultaneous measurement of activity across multiple brain regions in behaving animal...
Generalization in emergent communication has largely focused on novel inputs or linguistic structures, yet the capacity for agents to communicate with...
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA...