AI-Integrated IoT and Digital Twin Architecture for Smart Healthcare Industrial Automation and Real-Time Compliance Monitoring
DOI:
https://doi.org/10.15662/IJARCST.2025.0806026Keywords:
Artificial Intelligence, Internet of Things (IoT), Digital Twin Technology, Smart Healthcare, Industrial Automation, Predictive Analytics, Real-Time Monitoring, Compliance Management, Cyber-Physical SystemsAbstract
The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Digital Twin technologies is transforming modern industries by enabling intelligent monitoring, predictive analytics, and automated decision-making. In sectors such as healthcare and industrial automation, real-time data collection and analysis are essential for improving operational efficiency, ensuring regulatory compliance, and enhancing system reliability. IoT devices generate large volumes of sensor data, while digital twin models provide virtual representations of physical systems, allowing organizations to simulate, analyze, and optimize real-world processes. When combined with AI technologies, these systems can deliver predictive insights and automated responses that support smart and adaptive operations.
This research proposes an AI-integrated IoT and digital twin architecture designed for smart healthcare systems, industrial automation environments, and real-time compliance monitoring frameworks. The architecture leverages IoT sensors for continuous data acquisition, digital twin models for system simulation and performance monitoring, and machine learning algorithms for predictive analysis and intelligent decision-making. The proposed framework enables proactive fault detection, predictive maintenance, patient health monitoring, and automated regulatory compliance verification.
The study emphasizes the role of scalable cloud platforms, edge computing, and secure data management in supporting large-scale intelligent systems. The findings highlight the potential benefits of AI-driven digital twin ecosystems in improving operational transparency, safety, and efficiency while also addressing challenges related to system integration, data security, and infrastructure complexity.
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