Revolutionizing Healthcare: The Role of Machine Learning in IoMT

By: 1Simran Jaggi, 2Ricky Chandra Butarbutar

1Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India

2Department of computer science, Esa Unggul University, Indonesia


IoT has brought a great influence in various industries such as manufacturing, agriculture, management, and specifically, the Internet of Medical Things such as in healthcare systems. The significant surge in IoT technology is emphasized, with an estimated 1.2 trillion interconnected systems anticipated by 2025. The advancement of health systems is greatly influenced by the significant role of IoMT. Its seamless integration, real-time monitoring, and proactive health management contribute to this progress. The article discusses the challenges faced by IoMT, technological advancements made in this field, it’s challenges, applications, which emphasizes the need for improvements in smart medication and robust policies for security and data privacy.

Keywords: IOMT, Internet Of Things, Security, Protocols, Data Privacy, Medical Services, Remote Patient Monitoring, Machine Learning, Artificial Intelligence, Medical Internet of Things (MIot).


The convergence of IoT along with healthcare has given rise to the Internet of Medical Things, an approach that leverages interconnected devices to revolutionize healthcare [1]. It’s assessment framework and architecture (as shown in Figure 1 & 2) is as shown in Figure 1 and Figure 2. This article focuses on the transformative impact of Machine Learning (ML) within the IoMT landscape. With the proliferation of IoT technology, the IoMT is poised to connect 1.2 trillion systems by 2025, presenting a unique opportunity for ML to play a central role in optimizing healthcare outcomes.

Figure 1. IoMT Assessment Framework

Figure 2. IoMT Architecture

IoMT transforms healthcare, enhancing patient outcomes and delivery. Artificial intelligence and Deep Learning with cloud computing, ensures efficient healthcare with improved data analysis for medical advancements and diagnostics. The global adoption of IoT-based healthcare devices has increased rapidly, especially amid the pandemic. Machine Learning is crucial in healthcare, driving predictive analytics for disease trends, personalized treatment plans, and data-driven insights to optimize disease management [2].

Industrial Internet of Things (IOMT)[3] systems employ batch processing to optimize resources, execute tasks simultaneously, and organize data handling. This structured approach enhances productivity by managing data from networked devices in predetermined batches, improving workflows, reducing latency, and ensuring data integrity. IOMT’s batch processing simplifies large dataset management in industrial applications, enhancing scalability and system performance. Integration with the World Wide Web utilizes the Perception Layer for device data processing and diverse connectivity options through the Gateway Pile, from RFID to cloud and telecommunications technologies [4].

IoMT’s Role in Advancing Health Systems

IoMT [5] plays a crucial role in advancing health systems by seamlessly integrating interconnected devices, wearable devices, and smart applications. This transformative integration enables real-time monitoring and proactive health management, revolutionizing healthcare systems. IoMT’s four-layered architecture which incorporates sensors, cloud storage, blockchain, AI, and human-computer interaction, contributes to the evolution of intelligent healthcare solutions.

This transformative approach enhances diagnosis, treatment, and patient well-being, leading to efficient and patient-centric healthcare solutions. Simultaneously, Virtual, Mixed, and Augmented Reality (VR/MR/AR) as shown in Figure 3 offer significant potential in clinical therapy and education [6], [7]. Companies like XR-Health and EON Reality advance VR/AR platforms for telehealth, training, and collaboration, contributing to surgical training and accurate spinal surgeries.

Figure 3. Technologies integrated with IoMT to build smart healthcare system

The implementation of IoMT in healthcare as shown in Figure 4 ensures that by harnessing these technologies, IoMT enhances diagnostic capabilities, treatment effectiveness, and overall patient outcomes, offering a promising avenue for the advancement of healthcare systems[8].

Figure 4. Implementation of IoMT in Healthcare

Challenges of IOMT

The implementation of the Internet of Medical Things (IoMT) in healthcare has made significant strides but faces technical and social challenges as depicted in Figure 5[9]. Technical advancements are needed in smart medication and biomarkers, requiring the integration of technologies like 5G and blockchain to enhance telemedicine. Transparency and reliability in IoMT systems, particularly in AI interpretation, demand further development. Socially, IoMT optimizes healthcare costs but requires robust policies for security, interoperability, and data privacy[10]. Governments must drive IoMT development through incentives and ethical policy-making, ensuring patient privacy through secure data practices. Balancing economic, policy, and ethical considerations is essential for IoMT to fully benefit healthcare systems[11].

Figure 5. Challenges of IoMT

Application of IOMT

The applications of IoMT are impactful as shown in Figure 6. In healthcare, IoMT facilitates remote patient monitoring for patients and healthy populations, enabling real-time collection of physiological data for intelligent diagnoses and early disease detection. It plays a crucial role in infectious disease tracing, providing early warnings through the integration of IoMT and big data analytics. Additionally, IoMT contributes to the efficiency of smart hospitals, ensuring streamlined healthcare operations. Patient-centric applications measure the effectiveness of medical interventions and gather feedback, enhancing overall healthcare experiences and paving the way for more personalized and efficient healthcare solutions[12][13].

Figure 6. Applications of IOMT


The rapid growth of IoT technology, particularly in the healthcare sector with the emergence of the IoMT[14], signifies a transformation in the global health systems. The integration of interconnected devices, wearables, and smart applications has revolutionized healthcare delivery, enabling real-time monitoring, proactive health management, and intelligent diagnosis. The applications of IoMT, ranging from remote health monitoring to smart hospitals, showcase its diverse impact on patient outcomes and healthcare efficiency. While IoMT brings forth significant advancements, challenges such as technical complexities and the need for robust social policies must be addressed[15]. The seamless integration of artificial intelligence, deep learning, cloud computing, and other cutting-edge technologies underscores the potential of IoMT in shaping the future of healthcare. As we navigate through these challenges, a balanced approach considering economic, policy, and ethical dimensions will be crucial to unlocking the full potential of IoMT and ensuring its widespread benefits in advancing healthcare systems globally[16].

The article delves into advancements made in IoMT for precision insurance and rapid pandemic diagnoses. The IoMT[17] integrates extensive healthcare networks using advanced technologies like sensors, communication, parallel computing, and AI. IoMT’s key strengths lie in environment-awareness, networking, and intelligence, with future enhancements prioritizing security, data privacy, interpretability, multi-modality processing, and cost efficiency[18]. The integration of cloud computing, artificial intelligence, virtual reality, and the Internet of Medical Things notably enhances healthcare services, emphasizing remote monitoring, telemedicine, and robotics. A significant advancement is the impactful role of machine learning (ML) in healthcare; ML, continuously evolving, addresses challenges through early disease detection, patient management, and accelerated treatment development[19], [20].


  1. Qureshi, A., Batra, S., Vats, P., Singh, S., Phogat, M., & Sharma, A. K. (2022). A review of machine learning (ML) in the internet of medical things (IOMT) in the construction of a smart healthcare structure. Journal of Algebraic Statistics, 13(2), 225-231.
  2. Aminizadeh, S., Heidari, A., Toumaj, S., Darbandi, M., Navimipour, N. J., Rezaei, M., … & Unal, M. (2023). The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer Methods and Programs in Biomedicine, 107745.
  3. Mengi, G., Singh, S. K., Kumar, S., Mahto, D., & Sharma, A. (2021, September). Automated Machine Learning (AutoML): The Future of Computational Intelligence. In International Conference on Cyber Security, Privacy and Networking (pp. 309-317). Cham: Springer International Publishing.
  4. K. S. Sankaran, T. -H. Kim and P. N. Renjith, “An Improved AI-Based Secure M-Trust Privacy Protocol for Medical Internet of Things in Smart Healthcare System,” in IEEE Internet of Things Journal, vol. 10, no. 21, pp. 18477-18485, 1 Nov.1, 2023, doi: 10.1109/JIOT.2023.3280592.
  5. S. Lateef, M. Rizwan and M. A. Hassan, “Transformation in health-care services using Internet of Things (IoT): Review” in Big Data Analytics and Computational Intelligence for Cybersecurity, Cham, Switzerland:Springer, pp. 283-298, 2022.
  6. Huang, C., Wang, J., Wang, S., & Zhang, Y. (2023). Internet of medical things: A systematic review. Neurocomputing, 126719.
  7. Dwivedi, R., Mehrotra, D., & Chandra, S. (2022). Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. Journal of oral biology and craniofacial research, 12(2), 302-318.
  8. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022). Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58-73.
  9. Shakeel, T., Habib, S., Boulila, W., Koubaa, A., Javed, A. R., Rizwan, M., … & Sufiyan, M. (2023). A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects. Complex & intelligent systems, 9(1), 1027-1058.
  10. Vats, T., Singh, S. K., Kumar, S., Gupta, B. B., Gill, S. S., Arya, V., & Alhalabi, W. (2023). Explainable context-aware IoT framework using human digital twin for healthcare. Multimedia Tools and Applications, 1-25. Doi:
  11. Kumar, R., Singh, S. K., & Lobiyal, D. K. (2023). UPSRVNet: Ultralightweight, Privacy preserved, and Secure RFID-based authentication protocol for VIoT Networks. The Journal of Supercomputing, 1-28.
  12. Kumar, R., Sinngh, S. K., & Lobiyal, D. K. (2023, April). Routing of Vehicular IoT Networks based on various routing Metrics, Characteristics, and Properties. In 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 656-662). IEEE.
  13. Kumar, R., Singh, S. K., & Lobiyal, D. K. (2023). Communication structure for Vehicular Internet of Things (VIoTs) and review for vehicular networks. In Automation and Computation (pp. 300-310). CRC Press.
  14. Singh, I., Singh, S. K., Kumar, S., & Aggarwal, K. (2022, July). Dropout-VGG based convolutional neural network for traffic sign categorization. In Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 1 (pp. 247-261). Singapore: Springer Nature Singapore.
  15. Chopra, M., Kumar, S., Madan, U., & Sharma, S. (2021, December). Influence and establishment of smart transport in smart cities. In International Conference on Smart Systems and Advanced Computing (Syscom-2021).
  16. Sharma, A., Singh, S. K., Badwal, E., Kumar, S., Gupta, B. B., Arya, V., … & Santaniello, D. (2023, January). Fuzzy Based Clustering of Consumers’ Big Data in Industrial Applications. In 2023 IEEE International Conference on Consumer Electronics (ICCE) (pp. 01-03). IEEE.
  17. Kaur, P., Singh, S. K., Singh, I., & Kumar, S. (2021, December). Exploring Convolutional Neural Network in Computer Vision-based Image Classification. In International Conference on Smart Systems and Advanced Computing (Syscom-2021).
  18. Sharma, A., Singh, S. K., Kumar, S., Chhabra, A., & Gupta, S. (2021, September). Security of Android Banking Mobile Apps: Challenges and Opportunities. In International Conference on Cyber Security, Privacy and Networking (pp. 406-416). Cham: Springer International Publishing.
  19. Peñalvo, F. J. G., Maan, T., Singh, S. K., Kumar, S., Arya, V., Chui, K. T., & Singh, G. P. (2022). Sustainable Stock Market Prediction Framework Using Machine Learning Models. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-15.
  20. Gupta, M., & Singh, S. K. (2019). The internet of things: an overview of awareness, architecture and application. Int. J. Latest Trends Eng. Technol, 12, 19-24.
  21. Bhushan, K., & Gupta, B. B. (2017). Security challenges in cloud computing: state-of-art. International Journal of Big Data Intelligence4(2), 81-107.
  22. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-43.
  23. Gupta, B. B., Perez, G. M., Agrawal, D. P., & Gupta, D. (2020). Handbook of computer networks and cyber security. Springer10, 978-3.
  24. Zhang, Q., Guo, Z., Zhu, Y., Vijayakumar, P., Castiglione, A., & Gupta, B. B. (2023). A deep learning-based fast fake news detection model for cyber-physical social services. Pattern Recognition Letters168, 31-38.
  25. Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022). An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Transactions on Industrial Informatics18(11), 7946-7954.

Cite As

Jaggi S, Butarbutar RC (2024) Revolutionizing Healthcare: The Role of Machine Learning in IoMT, Insights2Techinfo, pp.1

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