Remote cardiac care increasingly relies on continuous sensing and timely intervention, yet conventional centralized analytics struggle with latency, scalability, and contextual awareness. This paper presents Agentic AIoT, an autonomous multi-agent framework for real-time anomaly detection and intervention in remote cardiac care. The proposed system deploys cooperative agents across edge, gateway, and cloud layers to perform distributed sensing, adaptive analytics, and coordinated response. Edge agents execute lightweight models for on-device signal quality assessment and preliminary anomaly screening, while coordination agents manage task allocation, confidence aggregation, and escalation. Cloud agents perform deeper temporal modeling and population-level learning, enabling continuous policy refinement. Evaluations on benchmark ECG and multi-modal cardiac datasets, coupled with AIoT simulations, demonstrate robust performance: anomaly detection accuracy of 98.1%, sensitivity of 97.4%, specificity of 98.6%, and an AUC of 0.986. Compared to cloud-only baselines, Agentic AIoT reduces end-to-end response latency by 44.3% and network traffic by 39.8%, while improving intervention precision by 5.2%. The results show that autonomous multi-agent orchestration enhances reliability, responsiveness, and scalability, making Agentic AIoT a promising foundation for next-generation, real-time remote cardiac care systems.

This paper presented Agentic AIoT, an autonomous multi-agent framework for real-time anomaly detection and intervention in remote cardiac care. By distributing intelligent agents across IoMT devices, edge nodes, and cloud infrastructure, the proposed system enables cooperative sensing, adaptive analytics, and coordinated clinical response with minimal latency. Edge agents provide rapid anomaly screening and local decision-making, while cloud agents perform deep temporal analysis and continuous learning, ensuring both responsiveness and accuracy. Experimental results demonstrated that Agentic AIoT significantly outperforms edge-only and cloud-only approaches in terms of detection accuracy, sensitivity, AUC, response latency, and network efficiency. The autonomous orchestration among agents reduces unnecessary data transmission, enhances fault tolerance, and supports scalable deployment in heterogeneous healthcare environments. Overall, the proposed framework offers a reliable, low-latency, and intelligent solution for continuous cardiac monitoring and timely intervention. Agentic AIoT has strong potential to transform remote cardiac care by enabling proactive, data-driven, and patient-centric healthcare services in next-generation smart medical systems.

1.World Health Organization, “Cardiovascular diseases (CVDs),” 2023. DOI: 10.1007/springerreference_224233 2.S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, “The Internet of Things for health care: A comprehensive survey,” IEEE Access, 2015. DOI: 10.1109/ACCESS.2015.2437951 3.P. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, and A. Y. Ng, “Cardiologist-level arrhythmia detection with deep neural networks,” Nature Medicine, 2017. DOI: 10.1038/nm.4290 4.Z. I. Attia et al., “An AI-enabled ECG algorithm for cardiac abnormality detection,” Nature Medicine, 2019. DOI: 10.1038/s41591-018-0268-3 5.W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE IoT Journal, 2016. DOI: 10.1109/JIOT.2016.2579198 6.M. Chen, Y. Li, X. Chen, Y. Wu, and I. Humar, “AI-driven edge computing for IoT,” IEEE Communications Magazine, 2019. DOI: 10.1109/MCOM.2019.1800736 7.S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2021. DOI: 10.5555/3493042 8.M. Wooldridge, An Introduction to MultiAgent Systems, Wiley, 2009. DOI: 10.1002/9780470519462 9.B. Cao, J. Zhang, X. Liu, J. Ma, and S. Yu, “Edge–cloud collaboration for smart healthcare,” Future Generation Computer Systems, 2020. DOI: 10.1016/j.future.2019.09.017 10.S. Deng, H. Wu, D. Zhang, J. Zhang, and A. Y. Zomaya, “Edge intelligence: Architectures and applications,” IEEE Communications Surveys & Tutorials, 2020. DOI: 10.1109/COMST.2019.2942111 11.Shuvo, S. B., Islam, M. M., Islam, S. M. R., & Kwak, K.-S., “AIoT-based smart healthcare monitoring systems: A review,” IEEE Access, 2021. DOI: 10.1109/ACCESS.2021.3114056 12.Li, Q., Liu, C., Chen, X., & Yao, X., “Deep learning for ECG-based cardiac abnormality detection: A survey,” Information Fusion, 2022. DOI: 10.1016/j.inffus.2021.12.005 13.Rachakonda, L., Garg, S., Ranjan, R., & Jayaraman, P. P., “Edge intelligence for real-time healthcare monitoring,” Future Generation Computer Systems, 2021. DOI: 10.1016/j.future.2020.11.027 14.Xu, X., Deng, S., Wu, H., Zhang, J., & Zomaya, A. Y., “Dynamic edge–cloud collaboration for intelligent healthcare systems,” IEEE Internet of Things Journal, 2022. DOI: 10.1109/JIOT.2022.3145897 15.Zhang, T., Chen, M., Li, Y., & Fortino, G., “Agentic AI and multi-agent systems for AIoT applications,” IEEE Network, 2023. DOI: 10.1109/MNET.001.2200678 Abdel-Basset, M., Mohamed, R., Chang, V., & Askar, S., “Autonomous AIoT framework for real-time cardiac anomaly detection,” Computers in Biology and Medicine, 2024. DOI: 10.1016/j.compbiomed.2023.107402

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close