Abstract

Digital health-based medical technology (m-health) uses mobile phones and other patient monitoring equipment to keep tabs on a patient's health. It is largely acknowledged as an important modern-era technological accomplishment. Traditionally, big data analytics and intelligent machines have been used in m-health to provide far more productive medical coverage. Current therapeutic research utilises a variety of data types, including electronic health records (EHRs), diagnostic images, and professional language that appear to be disparate, unclear, and disorganised. In addition, it makes a substantial contribution to the emergence of a large number of unstructured and jumbled data sources as a result of mobile platforms and healthcare infrastructure. The use of machine intelligence and big data analytics to enhance the mhealth infrastructure is thoroughly examined in this chapter. Additionally, various machine learning big data approaches and platforms are studied to the data source, methodology used, and application area. The overall findings of this study will undoubtedly affect the creation of techniques for processing m-health data more easily utilising a resource that incorporates big data and AI.