AI-Driven Compression and Redundancy Removal Techniques for Real-Time Communication in Healthcare Networks

Real-time communication of care patient searches and data exchange are key in healthcare networks. The explosion of medical data and more demand on efficient network performance have pushed to explore new compression techniques such as redundancy removal. We introduce an original application of artificial intelligence (AI) to improve data compression and redundancy in real-time healthcare communication. Here, we present an AI-Based framework that adapts compression algorithms on-the-fly with respect to network conditions and data properties which leads in the efficient utilization of bandwidth while reducing latency. To handle data more efficiently, we apply machine learning models in order to predict patterns and detect redundant information. We validate our design with extensive simulations, and real-world experiments showing substantial better performances in both network throughput and end-to-end response time over traditional solutions. Ultimately, this work helps to build healthcare communication systems that are more timely and robust, leading to improved patient outcomes and better operational efficiency.