Revolutionizing Disease Diagnosis: Integrating Artificial Intelligence and IOT In Smart Healthcare Systems
DOI:
https://doi.org/10.24113/ijoscience.v10i3.514Abstract
This research paper delves into the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionizing disease diagnosis within smart healthcare systems. AI, a cornerstone in computer science, is now pivotal in healthcare, offering sophisticated algorithms for analyzing medical data, thus aiding in decision-making and predictions. IoT extends this capability by enabling continuous data collection through web-enabled devices, including wearables and implanted sensors. The integration of AI and IoT in smart healthcare systems not only refines medical procedures but also enhances patient experiences and operational workflows. This paper explores how AI-driven routines in conjunction with IoT data streams facilitate rapid and accurate disease diagnosis, surpassing traditional methods often limited by human biases. The adoption of these technologies in healthcare promises to improve diagnostic accuracy, expedite processes, and broaden access to medical expertise, especially in remote areas. However, challenges such as ensuring data privacy, security, and ethical use of AI remain paramount.
Downloads
References
] Ratta, P., Kaur, A., Sharma, S., Shabaz, M., & Dhiman, G. (2021). Application of blockchain and internet of things in healthcare and medical sector: applications, challenges, and future perspectives. Journal of Food Quality, 2021, 1-20.
Awotunde, J. B., Folorunso, S. O., Bhoi, A. K., Adebayo, P. O., & Ijaz, M. F. (2021). Disease diagnosis system for IoT-based wearable body sensors with machine learning algorithm. Hybrid Artificial Intelligence and IoT in Healthcare, 201-222.
Pasluosta, C. F., Gassner, H., Winkler, J., Klucken, J., & Eskofier, B. M. (2015). An emerging era in the management of Parkinson's disease: wearable technologies and the internet of things. IEEE journal of biomedical and health informatics, 19(6), 1873-1881.
Jeong, J. S., Han, O., & You, Y. Y. (2016). A design characteristics of smart healthcare system as the IoT application. Indian Journal of Science and Technology, 9(37), 1-8.
Kadhim, K. T., Alsahlany, A. M., Wadi, S. M., & Kadhum, H. T. (2020). An overview of patient’s health status monitoring system based on internet of things (IoT). Wireless Personal Communications, 114(3), 2235-2262.
Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362-372.
Nadeem, O., Saeed, M. S., Tahir, M. A., & Mumtaz, R. (2020, December). A survey of artificial intelligence and internet of things (IoT) based approaches against COVID-19. In 2020 IEEE 17th international conference on smart communities: improving quality of life using ICT, IoT and AI (HONET) (pp. 214-218). IEEE.
Nayak, P., Mohapatra, S. K., & Sharma, S. C. M. (2022). Privacy and security issues in IoT cloud convergence of smart health care. In Connected e-Health: Integrated IoT and Cloud Computing (pp. 439-455). Cham: Springer International Publishing.
Awotunde, J. B., Jimoh, R. G., Folorunso, S. O., Adeniyi, E. A., Abiodun, K. M., & Banjo, O. O. (2021). Privacy and security concerns in IoT-based healthcare systems. In The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care (pp. 105-134). Cham: Springer International Publishing.
Ranjith, J., & Mahantesh, K. (2019, December). Privacy and security issues in smart health care. In 2019 4th International conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT) (pp. 378-383). IEEE.
Mansour, R. F., El Amraoui, A., Nouaouri, I., Díaz, V. G., Gupta, D., & Kumar, S. (2021). Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems. IEEE Access, 9, 45137-45146.
Amin, S. U., & Hossain, M. S. (2020). Edge intelligence and Internet of Things in healthcare: A survey. Ieee Access, 9, 45-59.
Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017). Advanced internet of things for personalised healthcare systems: A survey. Pervasive and mobile computing, 41, 132-149.
Mohanta, B., Das, P., & Patnaik, S. (2019, May). Healthcare 5.0: A paradigm shift in digital healthcare system using artificial intelligence, IOT and 5G communication. In 2019 International Conference on Applied Machine Learning (ICAML) (pp. 191-196). IEEE.
Guo, K., Lu, Y., Gao, H., & Cao, R. (2018). Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors, 18(5), 1341.
Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2019). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing, 10, 4151-4166.
Xie, Y., Lu, L., Gao, F., He, S. J., Zhao, H. J., Fang, Y., ... & 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, 1123-1133.
Tian, S., Yang, W., Le Grange, J. M., Wang, P., Huang, W., & Ye, Z. (2019). Smart healthcare: making medical care more intelligent. Global Health Journal, 3(3), 62-65.
Sujith, A. V. L. N., Sajja, G. S., Mahalakshmi, V., Nuhmani, S., & Prasanalakshmi, B. (2022). Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neuroscience Informatics, 2(3), 100028.
Barnawi, A., Chhikara, P., Tekchandani, R., Kumar, N., & Alzahrani, B. (2021). Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging. Future Generation Computer Systems, 124, 119-132.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Bhagwat Patel, Prof. Sanjay Kumar Pal

This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.