Harnessing Artificial Intelligence and Big Data for Transformative Healthcare Delivery

Abstract

Artificial Intelligence (AI), Big Data Analytics, Cloud-Computing & IoMT can help create a full change in the way health-care delivery is executed and assimilated in smart cities in healthcare. The convergence of Artificial Intelligence (AI) and the IoT (IoT) has given birth to a widely spread digital phenomenon known as the Internet of Medical Things (IoMT). IoMT only overcomes external resource limitations by extending device capabilities for better knowledge design based on efficiently fused context specifications. In-built AI technologies in digital health gadgets, as an instance of IoMT, can improve the quality of the health-care delivery system. Second, it also manages the enormous quantity of digital health data and addresses the incapacity of entities to examine the data and mine circumstances to draw evidence-based conclusions. The Internet of Medical Things (IoT) promotes and mandates the frictionless interactivity of more connected products and services. As an example, health-care delivery in smart cities focuses on improving the quality of life (QoL) of all the citizens of connected smart cities. A data-informed health-care delivery system, where health-care resources are fabricated based on the historical population pattern and demographic approach, can help to efficiently utilise scarce health-care resources and eliminate places and times devoid of a health-care resource.

Both Big Data and Health Informatics have become the buzzwords of Digital Health and the Health 4.0 trend during medical actions and clinical studies. The historical development of this field is first reviewed. The attractiveness for health stakeholders to efficiently manage the growing demand of patients’ data to be utilized for health alerts and disease predictions is pointed out. The issues such as data security and integration from heterogeneous data sources are reviewed as well as a two-tiered structure to combine shallow and deep learning as a solution framework is detailed with test cases in explorations of patients’ clinical records and remote health devices (IoMT). Overall, the hybrid techniques have a bright prospect to proceed with the AI innovation in the health area with many more applications and tests in practical scenarios.