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

This paper discusses a cutting-edge AI-powered compression solution that will improve healthcare network data transmission. The medical profession is becoming data-driven, so it's crucial to manage massive volumes of data, including patient records, medical pictures, and real-time monitoring, promptly and accurately. Complex compression methods that emphasize frequently used symbols enhance coding systems. The approach uses real-time tracking and adaptive programming to adjust to data conditions. It also handles errors well and employs machine learning to improve prediction. The AI-driven solution outperforms traditional methods in testing with 92% compression efficiency, 12 millisecond latency, and 99% data consistency. At only 4.5 watts, the approach may be sufficiently energy-efficient for mobile medical equipment. The recommended compression strategy solves healthcare communication issues, speeding up data exchange and improving patient outcomes. Finally, this strategy improves healthcare data management, transmission, and usage. It illustrates the importance of data communication in contemporary healthcare.