Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Path planning and optimization for unmanned aerial vehicles (UAVs)-assisted next-generation wireless networks is critical for mobility management and ensuring UAV safety and ubiquitous connectivity, especially in dense urban environments with street canyons and tall buildings. Traditional statistical and model-based techniques have been successfully used for path optimization in communication ne...
Traditional hospital-based medical examination methods face unprecedented challenges due to the aging global population. The Internet of Medical Things (IoMT), an advanced extension of the Internet of Things (IoT) tailored for the medical field, offers a transformative solution for delivering medical care. IoMT consists of interconnected medical devices that collect and transmit patients' vital ...
Federated Learning (FL) enables distributed training on edge devices but faces significant challenges due to resource constraints in edge environmen...
Cardiovascular diseases remain the leading cause of global morbidity and mortality. Validated risk scores are the basis of guideline-recommended care,...
Hospital-acquired infections (HAIs) significantly burden global healthcare systems, exacerbated by antibiotic-resistant bacteria. Traditional infectio...
Artificial intelligence (AI) has played a novel role in aiding healthcare system functions and enhancing the patient experience. Multidisciplinary tea...
Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic ...
In federated learning, fine-tuning pre-trained foundation models poses significant challenges, particularly regarding high communication cost and su...
Semantic communication has emerged as a promising paradigm for enhancing communication efficiency in sixth-generation (6G) networks. However, the br...
Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL met...
When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of p...
Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic ...
In recent years, the rapid development of machine learning has brought reforms and challenges to traditional communication systems. Semantic communi...
Glaucoma is an incurable ophthalmic disease that damages the optic nerve, leads to vision loss, and ranks among the leading causes of blindness worl...
Collaborative perception allows real-time inter-agent information exchange and thus offers invaluable opportunities to enhance the perception capabi...
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. Th...
Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditionally, federate...
Transformer-based foundation models (FMs) have recently demonstrated remarkable performance in medical image segmentation. However, scaling these mo...
Object detection shows promise for medical and surgical applications such as cell counting and tool tracking. However, its faces multiple real-world...
Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex networ...