The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

翻译：医疗物联网（H-IoT），通常称为数字医疗，是一种高度依赖智能传感设备（如血压监测仪、温度传感器等）以实现更快响应、治疗和诊断的数据驱动基础设施。然而，随着网络威胁形势的演变，物联网设备在面对更广泛的风险面（例如与生成式AI、5G-IoT相关的风险）时变得更加脆弱。一旦这些风险被利用，可能导致数据泄露、未授权访问、指挥控制能力缺失及潜在危害。本文综述了医疗物联网的基础知识，以及机器学习与H-IoT设备相关的隐私和数据安全挑战。文章进一步强调了监测医疗物联网各层（如感知层、网络层、云层和应用层）的重要性。异常检测与响应涉及多种网络攻击和协议，包括Wi-Fi 6、窄带物联网（NB-IoT）、蓝牙、ZigBee、LoRa和5G新无线电（5G NR）。为了保护和缓解H-IoT设备日益增长的网络安全漏洞，需要基于机器学习和深度学习技术的强健认证机制。因此，本综述论文探讨并报告了在H-IoT中构建韧性所需的安全与隐私挑战及风险缓解策略。