Aversano Lerina, Iammarino Martina, Mancino Ilaria, Montano Debora
Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, Foggia, Italy.
Department of Computer Science, University of Bari Aldo Moro, Bari, Italy.
PeerJ Comput Sci. 2024 Oct 21;10:e2232. doi: 10.7717/peerj-cs.2232. eCollection 2024.
In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients' health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.
在智能健康的背景下，可穿戴物联网（IoT）设备的使用越来越普遍，以便以更高效、个性化的方式监测和管理患者的健康状况。然而，选择最合适的人工智能（AI）方法来分析这些设备收集的数据，对于确保智能医疗应用的可靠性和有效性至关重要。此外，鉴于健康数据的敏感性和个人性质，保护其隐私和安全是一个日益受到关注的领域。在这种背景下，机器学习（ML）和深度学习（DL）作为成功的技术正在兴起，因为它们适用于医疗场景的高级分析和预测。因此，这项工作的目的是通过识别智能健康领域的挑战、最佳实践和未来机遇，为当前的文献状况做出贡献。我们旨在全面概述所使用的AI方法、采用的神经网络架构和所应用的算法，并研究与可穿戴物联网设备收集的健康数据管理相关的隐私和安全问题。通过这项系统综述，我们旨在为智能健康中AI解决方案的设计、开发和实施提供实用指南，以提高所提供护理的质量并促进患者福祉。为了实现我们的目标，我们选择并审查了几篇关注ML或DL网络架构的文章。最后的讨论突出了有待研究的研究空白，以及智能医疗中现有物联网应用的缺点和漏洞。
