The proliferation of wearable sensors, continuous monitoring devices, and Internet of Medical Things (IoMT) technologies has created unprecedented opportunities and challenges in healthcare. The global IoMT market, valued at approximately $65 billion in 2025, is projected to reach $155 billion by 2030, reflecting the rapid adoption of real-time health monitoring systems. These technologies generate massive volumes of high-frequency biomedical data streams that require real-time analysis for critical decision-making.

This special session addresses the computational and algorithmic challenges of processing, analyzing, and extracting actionable insights from streaming biomedical data. The shift from hospital-centric to patient-centric care, accelerated by telemedicine and wearable technologies, demands new computational paradigms that can process data on-the-fly while maintaining clinical accuracy and reliability.

This session bridges the gap between traditional bioinformatics and the emerging field of streaming health analytics. While genomic analysis deals with large but static datasets, IoMT systems produce endless streams of time-series data that must be analyzed with minimal latency. The unique challenges include:

• Temporal constraints requiring decisions in milliseconds to minutes
• Resource limitations on edge devices with limited computational power
• Concept drift as patient baselines change over time
• Balance between false negatives and alarm fatigue (ICU false alarm rates: 80-99%)