  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.
