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AI-Enabled Wearable Technology in Healthcare: Advancements, Challenges, and Future Directions
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The research examines AI-powered wearable technology in healthcare across current developments and obstacles and prospective paths needed. The healthcare industry experienced a revolution thanks to wearable devices that connect artificial intelligence systems. These devices enable continuous patient surveillance and improve disease prevention schemes and therapeutic outcomes. Advances in wearable sensor development working with AI algorithms have significantly improved their capability to monitor vital signs which leads to real-time health predictions. The technology faces three notable difficulties which involve data security risks along with performance constraints and regulatory requirements. Healthcare wearables have extensive potential to enhance both clinical results and healthcare spending with the incorporation of AI technologies despite existing problems. The advancement of predictive abilities in AI models and regulatory framework alignment for broader adoption remains the focus of future work, along with the refinement of data security techniques. Hospital-quality wearable devices contribute fundamentally to personal healthcare services by providing immediate feedback that helps patients reach superior disease management outcomes.
Keywords
AI Wearables, Healthcare Monitoring, Chronic Diseases, Predictive Analytics, Data Privacy, Patient Engagement
References
[1] Ahmed, A., Aziz, S., Abd-alrazaq, A., Farooq, F., & Sheikh, J. (2022). Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review. Journal of Medical Internet Research, 24(8), e36010. https://doi.org/10.2196/36010
[2] Dargazany, A. R., Stegagno, P., & Mankodiya, K. (2018). WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future. Mobile Information Systems, 2018, 1–20. https://doi.org/10.1155/2018/8125126
[3] Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2019). Wearable health technology and electronic health record integration: Scoping review and future directions. JMIR MHealth and UHealth, 7(9). https://doi.org/10.2196/12861
[4] Huarng, K.-H., Yu, T. H.-K., & Lee, C. fang. (2022). Adoption model of healthcare wearable devices. Technological Forecasting and Social Change, 174, 121286. https://doi.org/10.1016/j.techfore.2021.121286
[5] Junaid, S. B. (2022). Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare, 10(10), 1–45. https://doi.org/10.3390/healthcare10101940
[6] Mattison, G., Canfell, O., Forrester, D., Dobbins, C., Smith, D., Töyräs, J., & Sullivan, C. (2022). The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review. Journal of Medical Internet Research, 24(7), e36690. https://doi.org/10.2196/36690
[7] Ometov, A., Shubina, V., Klus, L., Skibińska, J., Saafi, S., Pascacio, P., Flueratoru, L., Gaibor, D. Q., Chukhno, N., Chukhno, O., Ali, A., Channa, A., Svertoka, E., Qaim, W. B., Casanova-Marqués, R., Holcer, S., Torres-Sospedra, J., Casteleyn, S., Ruggeri, G., & Araniti, G. (2021). A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges. Computer Networks, 193(108074), 108074. https://doi.org/10.1016/j.comnet.2021.108074
[8] Saraswat, D., et al. (2022). Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access, vol. 10, pp. 84486-84517. https://doi.org/10.1109/ACCESS.2022.3197671
[9] Shumba, A.-T., Montanaro, T., Sergi, I., Fachechi, L., De Vittorio, M., & Patrono, L. (2022). Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications. Sensors, 22(19), 7675. https://doi.org/10.3390/s22197675
[10] Wang, Y.-C., Xu, X., Hajra, A., Apple, S., Kharawala, A., Duarte, G., Liaqat, W., Fu, Y., Li, W., Chen, Y., & Faillace, R. T. (2022). Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics (Basel, Switzerland), 12(3), 689. https://doi.org/10.3390/diagnostics12030689
[11] Xie, Y., Lu, L., Gao, F., He, S., Zhao, H., Fang, Y., Yang, J., An, Y., Ye, Z., & Dong, Z. (2021). Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science, 41(6), 1123–1133. https://doi.org/10.1007/s11596-021-2485-0
[12] Chukwuebuka, N. a. J. (2022). Distributed machine learning pipelines in multi-cloud architectures: A new paradigm for data scientists. International Journal of Science and Research Archive, 5(2), 357–372. https://doi.org/10.30574/ijsra.2022.5.2.0049
[13] Jangid, J. (2020). Efficient Training Data Caching for Deep Learning in Edge Computing Networks.
How to cite this paper
@article{1703375,
author = {Fnu Zartashea},
title = {AI-Enabled Wearable Technology in Healthcare: Advancements, Challenges, and Future Directions},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {10},
pages = {342-353},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703375.pdf},
abstract = {The research examines AI-powered wearable technology in healthcare across current developments and obstacles and prospective paths needed. The healthcare industry experienced a revolution thanks to wearable devices that connect artificial intelligence systems. These devices enable continuous patient surveillance and improve disease prevention schemes and therapeutic outcomes. Advances in wearable sensor development working with AI algorithms have significantly improved their capability to monitor vital signs which leads to real-time health predictions. The technology faces three notable difficulties which involve data security risks along with performance constraints and regulatory requirements. Healthcare wearables have extensive potential to enhance both clinical results and healthcare spending with the incorporation of AI technologies despite existing problems. The advancement of predictive abilities in AI models and regulatory framework alignment for broader adoption remains the focus of future work, along with the refinement of data security techniques. Hospital-quality wearable devices contribute fundamentally to personal healthcare services by providing immediate feedback that helps patients reach superior disease management outcomes.},
keywords = {AI Wearables, Healthcare Monitoring, Chronic Diseases, Predictive Analytics, Data Privacy, Patient Engagement},
month = {April},
}