IoT in Healthcare: Infrastructure Challenges and Technical Feasibility for Adoption in Resource-Constrained Settings

Authors

DOI:

https://doi.org/10.63158/IJAIS.v3i1.72

Keywords:

Connectivity, Interoperability, Reliability, Internet of Things, Healthcare, Resource-constrained settings

Abstract

The integration of Internet of Things (IoT) technologies into healthcare is increasingly recognized as vital for improving patient outcomes, enhancing system efficiency, and addressing global health challenges. However, in resource constrained settings, adoption faces significant barriers that are often overlooked in existing literature. While prior studies emphasize IoT’s broad benefits, they provide limited empirical evidence on how devices perform under unstable connectivity, how they integrate with legacy hospital systems, and how they remain dependable amidst frequent power outages. This study therefore aimed to critically examine the infrastructure challenges and technical feasibility of IoT adoption in healthcare within such environments. Using the PRISMA methodology, a systematic review of peer reviewed articles published between 2021 and 2025 was conducted across IEEE Xplore, PubMed, Scopus, and Web of Science, focusing on connectivity, interoperability, and reliability. The findings revealed that unstable internet connectivity, poor system integration, and limited fault tolerance and power resilience are the most pressing challenges. The results underscore the need for context specific frameworks that strengthen connectivity resilience, enable seamless interoperability with fragmented systems, and ensure robust reliability safeguards. In conclusion, while IoT holds transformative potential for healthcare delivery, its sustainable adoption in resource constrained settings depends on innovative, resilient, and context driven solutions tailored to infrastructural realities.

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Published

2026-03-20

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How to Cite

IoT in Healthcare: Infrastructure Challenges and Technical Feasibility for Adoption in Resource-Constrained Settings. (2026). International Journal of Artificial Intelligence and Science, 3(1), 103-116. https://doi.org/10.63158/IJAIS.v3i1.72