Adaptation of IOT and AI technologies in Detecting Viral Infections and Cardiovascular Diseases
DOI:
https://doi.org/10.48001/978-81-966500-0-1-5Keywords:
Internet of Things (IoT), Artificial Intelligence (AI), SARS-CoV-2, Machine Learning ModelsAbstract
The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in healthcare has ushered in a new era of disease detection and management, significantly impacting the way viral and cardiovascular diseases are addressed. IoT devices, such as wearable sensors and environmental monitors, collect vast amounts of real-time data, which AI algorithms then analyze to detect early signs of infections or abnormalities. This synergy between IoT and AI has proven particularly effective in the early detection and monitoring of diseases like SARS-CoV-2, HIV, influenza, and various cardiovascular conditions. By leveraging AI's predictive analytics and machine learning models, healthcare providers can detect the impact of disease route, predict outbreaks, and mark treatment strategies with unprecedented accuracy. Despite the challenges of data privacy and integration into existing healthcare infrastructures, the advancements in IoT and AI have led to significant improvements in patient outcomes. These technologies are poised to play an increasingly central role in global health strategies, offering enhanced diagnostic capabilities, real-time monitoring, and personalized care solutions that can reduce the burden of disease and improve quality of life.
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References
Alshamrani, M. (2022). IoT and artificial intelligence implementations for remote health care monitoring systems: A survey. Journal of King Saud University- Computer and Information Sciences, 34(8), 4687–4701. https://doi.org/10.1016/j.jksuci.2021.06.005
Andarevi, M. H., & Iskandar, A. A. (2022). A Prototype of IoT-based Real-time Respiratory Rate Monitoring Using an Accelerometer Sensor. IBIOMED 2022- Proceedings of the 2022 4th International Conference on Biomedical Engineering, 42–46. https://doi.org/10.1109/IBIOMED56408.2022.9988053
Ani, R., Krishna, S., Anju, N., Sona, A. M., & Deepa, O. S. (2017). IoT based patient monitoring and diagnostic prediction tool using ensemble classifier. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017 Janua, 1588–1593. https://doi.org/10.1109/ICACCI.2017.8126068
Annolino, H. (2022). Leveraging predictive analytics to reduce influenza and COVID-19-related adverse events. Nursing, 52(3), 35–37. https://doi.org/10.1097/01.NURSE.0000806160.64587.92
Christakis, N. A., & Fowler, J. H. (2010). Social network sensors for early detection of contagious outbreaks. PLoS ONE, 5(9), 1–8. https://doi.org/10.1371/journal.pone.0012948
Fusco, R., Grassi, R., Granata, V., Setola, S. V., Grassi, F., Cozzi, D., Pecori, B., Izzo, F., & Petrillo, A. (2021). Artificial intelligence and covid-19 using chest ct scan and chest x-ray images: Machine learning and deep learning approaches for diagnosis and treatment. Journal of Personalized Medicine, 11(10). https://doi.org/10.3390/jpm11100993
Guo, W., Lv, C., Guo, M., Zhao, Q., Yin, X., & Zhang, L. (2023). Innovative applications of artificial intelligence in zoonotic disease management. Science in One Health, 2. https://doi.org/10.1016/j.soh.2023.100045
Hinton, G. (2018). Deep learning-a technology with the potential to transform health care. JAMA- Journal of the American Medical Association, 320(11), 1101–1102.https://doi.org/10.1001/jama.2018.11100
Khan, J. I., Khan, J., Ali, F., Ullah, F., Bacha, J., & Lee, S. (2022). Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3181605
Ko, J., Swetter, S. M., Blau, H. M., Esteva, A., Kuprel, B., Novoa, R. A., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. http://dx.doi.org/10.1038/nature21056
Krishnan, D. S. R., Gupta, S. C., & Choudhury, T. (2018). An IoT based Patient Health Monitoring System. International Conference on Advances in Computing and Communication Engineering, ICACCE, 1–7. https://doi.org/10.1109/ICACCE.2018.8441708
Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. Information Systems Frontiers, 23(6), 1467–1497. https://doi.org/10.1007/s10796-021-10131-x
Wu, Z., McGoogan, J. M., Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X., & Peng, Z. (2020). Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus Infected Pneumonia in Wuhan, China. JAMA- Journal of the American Medical Association, 323(13), 1239–1242.
