English: The convergence of artificial intelligence with communication networks and electronic systems is transforming how healthcare is sensed, transmitted, and delivered. Wearable and implantable devices, wireless body area networks, and health-oriented Internet of Things (IoT) deployments generate continuous streams of physiological data whose value depends on advances squarely within electronics and communication engineering: energy-efficient integrated circuits, robust biomedical signal processing, low-latency and reliable wireless links, and intelligent network architectures. This track provides a forum at WECE 2026 for research in which AI and healthcare form the application layer while electronics, signal processing, and communications form the technical core. Topics of interest include AI-assisted biomedical signal processing (e.g., ECG/EEG/PPG denoising, feature extraction, and compression), edge AI and low-power hardware for on-device health inference, wireless body area networks and wearable/implantable sensor communication, 5G/6G and IoT architectures for telemedicine and remote patient monitoring, and machine learning for physiological data transmission over resource-constrained links. By requiring every contribution to demonstrate a genuine electronics, signal processing, or communications contribution, the track aligns tightly with the scope of WECE 2026 and the expertise of its technical program committee. The track aims to attract 5–8 or more high-quality submissions and 1–3 invited talks, foster interdisciplinary exchange between the ECE and digital-health communities, and identify open challenges in building intelligent, connected, and trustworthy health monitoring systems for modern society.

中文: 人工智能与通信网络和电子系统的融合正在改变医疗健康的感知、传输和交付方式。可穿戴和植入式设备、无线体域网以及面向健康的物联网部署生成连续的生理数据流，其价值取决于电子与通信工程领域的进步：高能效集成电路、鲁棒的生物医学信号处理、低延迟可靠的无线链路以及智能网络架构。本Track为WECE 2026提供了一个论坛，其中AI和医疗健康构成应用层，而电子学、信号处理和通信构成技术核心。感兴趣的主题包括AI辅助的生物医学信号处理（如ECG/EEG/PPG去噪、特征提取和压缩）、用于设备端健康推理的边缘AI和低功耗硬件、无线体域网和可穿戴/植入式传感器通信、用于远程医疗和远程患者监测的5G/6G和IoT架构，以及资源受限链路上生理数据传输的机器学习。通过要求每篇投稿都展示真正的电子学、信号处理或通信贡献，本Track与WECE 2026的范围及其技术程序委员会的专业知识紧密契合。本Track旨在吸引5-8篇或更多高质量投稿和1-3场特邀报告，促进ECE与数字健康社区之间的跨学科交流，并确定构建现代社会中智能、互联和可信赖的健康监测系统所面临的开放挑战。