Healthcare System
Healthcare systems are undergoing a digital transformation driven by the need to improve efficiency, equity, and patient outcomes. Current research focuses on leveraging machine learning, particularly deep learning models (like Residual Networks and Transformers) and large language models (LLMs), to analyze diverse data sources (electronic health records, medical images, wearable sensor data) for predictive analytics, personalized interventions, and improved diagnostics. This work emphasizes addressing biases and ensuring fairness in algorithms, as well as enhancing data privacy and security through techniques like federated learning and data-free quantization. The ultimate goal is to create more efficient, equitable, and effective healthcare delivery systems through data-driven insights and AI-powered tools.
Papers
A Survey of Embodied AI in Healthcare: Techniques, Applications, and Opportunities
Yihao Liu, Xu Cao, Tingting Chen, Yankai Jiang, Junjie You, Minghua Wu, Xiaosong Wang, Mengling Feng, Yaochu Jin, Jintai Chen
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
Subrata Kumer Paul, Abu Saleh Musa Miah, Rakhi Rani Paul, Md. Ekramul Hamid, Jungpil Shin, Md Abdur Rahim