用于早期检测慢性病和传染病传播的边缘人工智能:机遇、挑战与未来方向

IF 2.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Future Internet Pub Date : 2023-11-18 DOI:10.3390/fi15110370
E. Badidi
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引用次数: 0

摘要

边缘人工智能是一种利用边缘设备实现分布式智能的跨学科技术,正迅速成为早期健康预测的关键组成部分。边缘人工智能包括数据分析和人工智能(AI),使用机器学习、深度学习和联合学习模型,在远离集中数据中心的网络边缘部署和执行。通过人工智能,可以对电子健康记录、可穿戴设备和人口信息等多种来源的大型数据集进行仔细分析,从而识别复杂的模式并预测个人的未来健康状况。联合学习是人工智能领域的一种新方法,通过在分布式边缘设备上对人工智能模型进行协作训练,进一步增强了这种预测能力,同时还能维护隐私。利用边缘计算,可以在本地处理和分析数据,减少延迟并实现即时决策。本文回顾了边缘人工智能在早期健康预测中的作用,并强调了其改善公共卫生的潜力。涉及的主题包括使用人工智能算法早期检测糖尿病和癌症等慢性疾病,以及在可穿戴设备中使用边缘计算检测传染病的传播。除了讨论边缘人工智能在早期健康预测方面的挑战和局限性外,本文还强调了未来的研究方向,以解决这些问题,并与现有的医疗保健系统进行整合,充分挖掘这些技术在改善公共健康方面的潜力。
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Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.
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来源期刊
Future Internet
Future Internet Computer Science-Computer Networks and Communications
CiteScore
7.10
自引率
5.90%
发文量
303
审稿时长
11 weeks
期刊介绍: Future Internet is a scholarly open access journal which provides an advanced forum for science and research concerned with evolution of Internet technologies and related smart systems for “Net-Living” development. The general reference subject is therefore the evolution towards the future internet ecosystem, which is feeding a continuous, intensive, artificial transformation of the lived environment, for a widespread and significant improvement of well-being in all spheres of human life (private, public, professional). Included topics are: • advanced communications network infrastructures • evolution of internet basic services • internet of things • netted peripheral sensors • industrial internet • centralized and distributed data centers • embedded computing • cloud computing • software defined network functions and network virtualization • cloud-let and fog-computing • big data, open data and analytical tools • cyber-physical systems • network and distributed operating systems • web services • semantic structures and related software tools • artificial and augmented intelligence • augmented reality • system interoperability and flexible service composition • smart mission-critical system architectures • smart terminals and applications • pro-sumer tools for application design and development • cyber security compliance • privacy compliance • reliability compliance • dependability compliance • accountability compliance • trust compliance • technical quality of basic services.
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