  In recent years, smart healthcare IoT devices have become ubiquitous, but
they work in isolated networks due to their policy. Having these devices
connected in a network enables us to perform medical distributed data analysis.
However, the presence of diverse IoT devices in terms of technology, structure,
and network policy, makes it a challenging issue while applying traditional
centralized learning algorithms on decentralized data collected from the IoT
devices. In this study, we present an extensive review of the state-of-the-art
machine learning applications particularly in healthcare, challenging issues in
IoT, and corresponding promising solutions. Finally, we highlight some
open-ended issues of IoT in healthcare that leaves further research studies and
investigation for scientists.

Machine Learning;Computers and Society