A Literature Review on Internet of Medical Things

Year : 2025 | Volume : 03 | Issue : 02 | Page : 23 34
    By

    Shalini Sachdeva,

  • Rajesh Sachdeva,

  1. Assistant Professor, Department of Computer Science and Applications, Ram Sukh Das College Ferozepur, Firozpur, Punjab, India
  2. Assistant Professor, Department of Computer Science, Dev Samaj College for Women, Firozpur, Punjab, India

Abstract

Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment planning, and patient management through data-driven insights and automation. The Internet of Medical Things (IoMT) represents a significant shift in modern healthcare, enabling real-time patient monitoring, data-driven decision-making, and enhanced medical outcomes. This literature review explores the architecture of IoMT, including perception layer, network layer, transport layer and application layer. It also thoroughly explores key challenges like ensuring data security, meeting regulatory requirements, and achieving seamless system interoperability. By analyzing recent studies and emerging trends, this review provides a comprehensive understanding of IoMT’s role in transforming healthcare delivery. It examines key technologies, including wearable sensors, remote monitoring systems, and AI-driven analytics, while also addressing critical concerns such as cybersecurity, interoperability, and data privacy. Moreover, this study emphasizes recent research on how the Internet of Medical Things (IoMT) influences patient health outcomes, enhances healthcare efficiency, and contributes to lowering costs.

Keywords: Confidentiality, artificial intelligence, IoMT, integrity, security, wearable devices

[This article belongs to International Journal of Wireless Security and Networks ]

How to cite this article:
Shalini Sachdeva, Rajesh Sachdeva. A Literature Review on Internet of Medical Things. International Journal of Wireless Security and Networks. 2025; 03(02):23-34.
How to cite this URL:
Shalini Sachdeva, Rajesh Sachdeva. A Literature Review on Internet of Medical Things. International Journal of Wireless Security and Networks. 2025; 03(02):23-34. Available from: https://journals.stmjournals.com/ijwsn/article=2025/view=232825


References

  1. Fotouhi H, Causevic A, Lundqvist K, Bjorkman M. Communication and security in Health Monitoring Systems — a review. 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). 2016; 545–554. https://doi.org/10.1109/compsac.2016.8
  2. Satyajit Sinha. (2024 Sep 3). State of IOT 2022: Number of connected IOT devices growing 18% to 14.4 billion globally. [Online]. IoT Analytics. Retrieved February 20, 2023, from https://iot-analytics.com/number-connected-iot-devices/
  3. Cogniteq. (2022 Jan 20). Internet of medical things (IOMT): Innovative Future for Healthcare Industry. [Online]. Cogniteq. Retrieved February 20, 2023, from http://www.cogniteq.com/blog/ internet-medical-things-iomt-innovative-future-healthcare-industry
  4. Newman LH. (2022 Mar 8). Critical bugs expose hundreds of thousands of medical devices and atms. [Online]. Wired. Retrieved February 20, 2023, from https://www.wired.com/story/access7-iot- vulnerabilities-medical-devices-atms/
  5. Ravi V, Alazab M, Selvaganapathy S, Chaganti R. A multi-view attention-based deep learning framework for malware detection in Smart Healthcare Systems. Comput Commun. 2022; 195: 73–81. https://doi.org/10.1016/j.comcom.2022.08.015
  6. Radoglou-Grammatikis P, Sarigiannidis P, Efstathopoulos G, Lagkas T, Fragulis G, Sarigiannidis A. A self-learning approach for detecting intrusions in Healthcare Systems. ICC 2021 – IEEE International Conference on Communications. 2021; 1–6. https://doi.org/10.1109/icc42927.2021. 9500354
  7. Ghubaish A, Salman T, Zolanvari M, Unal D, Al-Ali A, Jain R. Recent advances in the internet-of-medical-things (IOMT) systems security. IEEE Internet Things J. 2021; 8(11): 8707–8718. https://doi.org/10.1109/jiot.2020.3045653.
  8. Hady AA, Ghubaish A, Salman T, Unal D, Jain R. Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access. 2020; 8: 106576–106584. https://doi.org/10.1109/access.2020.3000421
  9. Clifton L, Clifton DA, Pimentel MA, Watkinson PJ, Tarassenko L. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE J Biomed Health Inform. 2014; 18(3): 722–730. https://doi.org/10.1109/jbhi.2013.2293059
  10. Rani AA, Baburaj E. Secure and intelligent architecture for cloud-based healthcare applications in Wireless Body Sensor Networks. Int J Biomed Eng Technol. 2019; 29(2): 186–199. https://doi.org/10.1504/ijbet.2019.097305
  11. Chakraborty S, Aich S, Kim H-C. A secure healthcare system design framework using Blockchain technology. 2019 21st International Conference on Advanced Communication Technology (ICACT). 2019; 260–264. https://doi.org/10.23919/icact.2019.8701983
  12. Alabdulatif A, Khalil I, Forkan AR, Atiquzzaman M. Real-time secure health surveillance for Smarter Health Communities. IEEE Commun Mag. 2019; 57(1): 122–129. https://doi.org/10.1109/ mcom.2017.1700547
  13. Tao H, Bhuiyan MZ, Abdalla AN, Hassan MM, Zain JM, Hayajneh T. Secured data collection with hardware-based ciphers for IOT-based healthcare. IEEE Internet Things J. 2019; 6(1): 410–420. https://doi.org/10.1109/jiot.2018.2854714
  14. Jiong Zhang, Zulkernine M, Haque A. Random-forests-based network intrusion detection systems. IEEE Trans Syst Man Cybern Part C (Appl Rev). 2008; 38(5): 649–659. https://doi.org/10.1109/ tsmcc.2008.923876
  15. Rao BB, Swathi K. Fast knn classifiers for network Intrusion Detection System. Indian J Sci Technol. 2017; 10(14): 1–10. https://doi.org/10.17485/ijst/2017/v10i14/93690
  16. Shapoorifard H, Shamsinejad P. Intrusion detection using a novel hybrid method incorporating an improved KNN. Int J Comput Appl. 2017; 173(1): 5–9. https://doi.org/10.5120/ijca2017914340
  17. Rathore H, Al-Ali AK, Mohamed A, Du X, Guizani M. A novel deep learning strategy for classifying different attack patterns for deep brain implants. IEEE Access. 2019; 7: 24154–24164. https://doi.org/10.1109/access.2019.2899558
  18. Yaacoub J-PA, Noura M, Noura HN, Salman O, Yaacoub E, Couturier R, Chehab A. Securing internet of medical things systems: Limitations, issues and recommendations. Future Gener Comput Syst. 2020; 105: 581–606. https://doi.org/10.1016/j.future.2019.12.028
  19. Saba T. Intrusion detection in Smart City Hospitals using ensemble classifiers. 2020 13th International Conference on Developments in ESystems Engineering (DeSE). 2020; 418–422. https://doi.org/10.1109/dese51703.2020.9450247
  20. Kumar P, Gupta GP, Tripathi R. An ensemble learning and fog-cloud architecture- driven cyber-attack detection framework for IOMT networks. Comput Commun. 2021; 166: 110–124. https://doi.org/10.1016/j.comcom.2020.12.003

Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 26/05/2025
Accepted 05/08/2025
Published 09/09/2025
Publication Time 106 Days


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