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@article{204450,
        author = {Austy B. Evangeline and S. Anub Sathya},
        title = {AI-Driven Digital Twin Framework for Intelligent Healthcare Monitoring Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {98-102},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204450},
        abstract = {The rapid advancement of Internet of Things (IoT) technologies has enabled continuous and remote healthcare monitoring through wearable and connected medical devices. However, real-time analysis, predictive capabilities, and personalized healthcare remain challenging due to the dynamic nature of physiological data. This paper proposes an AI-driven Digital Twin framework for smart healthcare monitoring, where a virtual representation of a patient is continuously updated using real-time sensor data. The proposed system integrates IoT-based data acquisition, cloud/edge computing, and artificial intelligence models to simulate patient conditions and predict potential health risks. Machine learning techniques are employed to analyze vital parameters such as heart rate, body temperature, and oxygen levels, enabling early detection of anomalies. The Digital Twin model supports personalized treatment recommendations and proactive healthcare management. Experimental analysis demonstrates improved accuracy in anomaly detection and reduced response time compared to traditional monitoring systems. The proposed framework enhances reliability, scalability, and efficiency, making it suitable for next-generation smart healthcare applications.},
        keywords = {Digital Twin, Smart Healthcare, IoT, Artificial Intelligence, Remote Monitoring, Predictive Analytics.},
        month = {June},
        }