Track 1： AI & Health Engineering | 人工智能与健康工程

数字健康工程涉及领域广泛，如设施健康、设备健康和生命健康等领域，而且面向多个数字技术的应用场景，如数字化健康诊断与评估、数字化健康预警、预测与干预等。数字健康工程属于跨学科的研究范畴，在提高健康效率与公平方面扮演者重要的角色。本次分论坛将充分交流人工智能技术赋能全社会健康发展与安全，找到促进可持续发展的关键路径。

Digital Health Engineering covers a wide range of fields, such as facility health, equipment health, and life health, and is suitable for multiple digital technologies applications scenarios, such as digital health diagnostics and assessment, digital health early warning, prediction and intervention, etc. Digital health engineering is an interdisciplinary research field that plays an important role in improving health efficiency and equity. In this session, we will communicate the using of artificial intelligence to empower the healthy development of the whole society, and to explore the important routine for sustainable development.

• 人工智能与设施健康 / AI & Facility Health
• 人工智能与设备健康 / AI & Equipment Health
• 人工智能与生命健康 / AI & Life Health
• 人工智能与项目健康 / AI & Project Healthcare

Track 2： Big Data Analytics and Management | 大数据分析与处理

Focuses on theories, models, and systems for collecting, storing, analyzing, and managing large-scale data, as well as real-world applications across industries.

聚焦大规模数据的采集、存储、分析与管理相关的理论、模型与系统，及其在各行业中的实际应用。

Track 3： Machine Learning and Intelligent Systems | 机器学习与智能系统

Covers machine learning algorithms, deep learning, and the design of intelligent systems that perceive, reason, and act in complex environments.

涵盖机器学习算法、深度学习，以及在复杂环境中具备感知、推理与执行能力的智能系统设计。

Track 4： Computer Vision and Pattern Recognition | 计算机视觉与模式识别

Addresses image and video analysis, object detection and recognition, and pattern recognition methods for intelligent perception.

关注图像与视频分析、目标检测与识别，以及面向智能感知的模式识别方法。