AI-Integrated IoT Antenna Systems based Remote Health Monitoring for Technical Reliability, Insurance Coverage, Medical Liability, and Biomedical Data Governance

Abstract

The study explore how AI-based IoT antenna systems can improve remote health monitoring, and more specifically technical reliability, insurance efficiency, medical liability, and biomedical data governance. The quantitative research design was used and primary data was gathered using a structured questionnaire that was used on 250 respondents who included healthcare workers, insurance workers, IT professionals and system users. The statistical analysis of the data was done through descriptive analysis, reliability test, correlation, and regression analysis using SPSS. The results show that the use of AI-IoT systems has a considerable positive effect on the four dimensions of a remote healthcare system. Biomedical data governance provided the strongest effect, with technical reliability coming in second, which means that AI-enabled systems lead to considerable improvements in data security and privacy and are more efficient in real-time monitoring. In the efficiency of insurance, the moderate results were observed, which implies better processing of claims and anti-fraud activities. Nevertheless, the impact on medical liability was relatively less, which underscores the issue of legal responsibility in AI-led healthcare settings. The study makes a contribution to the already existing body of literature on digital healthcare by offering empirical information about the multi-dimensional effectiveness of AI-IoT integration. The findings highlight the importance of powerful regulatory systems and policy solutions to combat new issues of legal responsibility and data management. The results provide useful information to policymakers, health service providers, and insurance agencies on how to maximize the use of AI-based remote health monitoring systems.