用轻量可解释AI实现低延迟实时决策支持，提升边缘设备效率
Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models
- 采用轻量级可解释模型与边缘计算结合，降低延迟
- 验证了在资源受限下仍能保持高效决策能力
- 适合物联网与实时医疗等对响应速度要求高的场景
本文研究利用低延迟人工智能模型的实时决策支持系统，整合了全链路AI驱动决策工具、与边缘-物联网技术的融合，以及人机协同的有效方法。探讨了大语言模型在资源受限环境下的辅助决策作用，分析了DeLLMa等新技术、模型压缩方法及边缘设备上分析性能的提升。同时关注资源有限性与灵活框架的需求。通过深入综述，提出开发策略与应用场景建议，推动更高效、灵活的AI支持系统发展，为该快速演进领域指明未来突破方向，凸显AI重塑实时决策支持的潜力。
原文摘要 · Abstract (English)
This paper investigates real-time decision support systems that leverage low-latency AI models, bringing together recent progress in holistic AI-driven decision tools, integration with Edge-IoT technologies, and approaches for effective human-AI teamwork. It looks into how large language models can assist decision-making, especially when resources are limited. The research also examines the effects of technical developments such as DeLLMa, methods for compressing models, and improvements for analytics on edge devices, while also addressing issues like limited resources and the need for adaptable frameworks. Through a detailed review, the paper offers practical perspectives on development strategies and areas of application, adding to the field by pointing out opportunities for more efficient and flexible AI-supported systems. The conclusions set the stage for future breakthroughs in this fast-changing area, highlighting how AI can reshape real-time decision support.
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