SECURE AND INTELLIGENT HEALTHCARE SYSTEMS WITH IOMT: BLOCK CHAIN AND INTEROPERABILITY CHALLENGES
DOI:
https://doi.org/10.4238/bvj91m06Keywords:
Internet of Medical Things (IoMT); Artificial Intelligence; Blockchain Technology; Healthcare Cybersecurity; Interoperability; Fast Healthcare Interoperability Resources (FHIR).Abstract
As a result of the rapid development of digital technologies within the healthcare field, the industry is now taking advantage of intelligent medical systems to monitor patients continuously and make data-based clinical decisions. The deployment of the Internet of Medical Things (IoMT), artificial intelligence advanced healthcare information system have greatly improved the productivity and access to the delivery of healthcare services today compared to what they were previously. However, while these technologies are increasing productivity and providing greater access to healthcare, they also create significant challenges concerning cyber security, data privacy, systems compatibility, and scalability due to the growing amount of connected medical devices and data-sharing platforms employed within the healthcare industry. New standards such as Fast Healthcare Interoperability Resources (FHIR) and new technologies such as block chain have been created to address these issues. These new standards and technologies facilitate secure data-sharing, greater transparency, and the ability to have fluid communication across different healthcare systems. Many studies have been conducted on these individual topics; however, most studies have focused on singular topics and do not provide an integrated approach to the three areas of security, interoperability, and intelligent decision-making. In this paper, we will review the latest literature on block chain-based healthcare systems, cyber security practices, and interoperability frameworks recognized within artificial intelligence (AI)-enabled IoMT healthcare environments. We also discuss the limitations of current approaches, including difficulties in real-world implementation, high computational cost, and legal and regulatory issues. The results of this study provide useful directions for future research to build secure, scalable, and interoperable intelligent healthcare systems.
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