Intelligent Telemedicine Systems: Machine Learning for Remote Diagnosis and Patient Management
Article Sidebar
Main Article Content
Abstract
Telemedicine has experienced significant evolution in the past 10 years from a communication-driven health service to an intelligent, data-driven environment driven by AI (artificial intelligence), ML (machine learning), IoMT (Internet of Medical Things) and cloud–edge computing. This development has been further advanced through ongoing global health challenges that have seen an ageing population, rising burden of chronic, multi-morbid conditions, healthcare specialist shortage, soaring cost of healthcare and major disruptions like the COVID-19 pandemic. Modern telehealth services are now integrating more machine learning algorithms for remote diagnosis, real-time monitoring the patient, predictive risk stratification and proactive treatment. This paper offers a comprehensive academic overview of intelligent telemedicine systems using machine learning for remote diagnosis and patient management. It synthesises and reviews recent literature on image-based diagnostics, physiological time-series modelling, NLP algorithms used to interpret symptoms, and multimodal data fusion models. An integrated system architecture is proposed and presented to illustrate how IoMT devices, data collection pipelines, preprocessing layers, machine learning inbound engines, and clinician decision tools are interlinked in the digital telemedicine landscape. We evaluated a proof-of-concept machine learning workflow for short-term patient deterioration prediction, relying on synthetic IoMT-style patient datasets to demonstrate real-world feasibility and clinical relevance. Apart from technical performance, the paper also describes the challenges of data quality, interoperability, algorithmic bias, explainability, cybersecurity, regulatory compliance, and integration into clinical workflows. Ethical questions, including trust in intelligent healthcare, responsibility, patients’ autonomy, and fair treatment, are raised. Finally, opportunities for future research are mentioned, including digital twins, federated learning, explainable AI, autonomous IoMT ecosystems, and emotionally intelligent telemedicine platforms to guide researchers, clinicians, systems designers, and policy makers on the evolution of telemedicine in a way that is intelligent, proactive and just in health care.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- 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;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
[1] L. C. Schünke, B. Mello, C. A. da Costa, R. S. Antunes, S. J. Rigo, G. de O. Ramos, R. da R. Righi, J. N. Scherer, and B. Donida, “A rapid review of machine learning approaches for telemedicine in the scope of COVID-19,” Artif. Intell. Med., vol. 129, p. 102312, 2022, doi: 10.1016/j.artmed.2022.102312.
[2] N. M. Sarkar, N. R. Dey, and N. M. T. Mia, “Artificial intelligence in telemedicine and remote patient monitoring: Enhancing virtual healthcare through AI-driven diagnostic and predictive technologies,” Int. J. Sci. Res. Arch., vol. 15, no. 2, pp. 1046–1055, 2025, doi: 10.30574/ijsra.2025.15.2.1402.
[3] M. Rossi and S. Rehman, “Integrating artificial intelligence into telemedicine: Evidence, challenges, and future directions,” Cureus, vol. 17, no. 8, 2025, doi: 10.7759/cureus.90829.
[4] U. Chaturvedi, S. B. Chauhan, and I. Singh, “The impact of artificial intelligence on remote healthcare: Enhancing patient engagement, connectivity, and overcoming challenges,” Intelligent Pharmacy, vol. 3, no. 5, pp. 323–329, 2025, doi: 10.1016/j.ipha.2024.12.003.
[5] S. Khadse, P. Verma, and V. Lande, “Artificial intelligence and machine learning in telemedicine: Transforming remote healthcare,” in Proc. Int. Conf. Mach. Learn. Auton. Syst. (ICMLAS), 2025, pp. 508–514, doi: 10.1109/ICMLAS64557.2025.10968984.
[6] P. Sharma, “Smart healthcare: The role of AI, robotics, and NLP in advancing telemedicine and remote patient monitoring,” BMC Artif. Intell., vol. 1, no. 1, p. 14, 2025, doi: 10.1186/s44398-025-00015-4.
[7] O. Baker, Z. Ziran, M. Mecella, K. Subaramaniam, and S. Palaniappan, “Predictive modeling for pandemic forecasting: A COVID-19 study in New Zealand and partner countries,” Int. J. Environ. Res. Public Health, vol. 22, no. 4, p. 562, 2025, doi: 10.3390/ijerph22040562.
[8] J. G. Fernandes, “Artificial intelligence in telemedicine,” in Artificial Intelligence in Medicine, Cham, Switzerland: Springer, 2022, pp. 1219–1227, doi: 10.1007/978-3-030-64573-1_93.
[9] O. Baker, Z. Ziran, M. Mecella, and K. Subaramaniam, “AI-driven melanoma detection in New Zealand: A ResNet50-based approach,” Inform. Med. Unlocked, vol. 58, p. 101697, 2025, doi: 10.1016/j.imu.2025.101697.
[10] S. Palaniappan, R. Logeswaran, K. Subaramaniam, O. Baker, and B. N. Dung, “Training the brain: A machine learning approach to predicting wellbeing through intentional thought pattern modification,” J. Inform. Web Eng., vol. 4, no. 3, pp. 64–89, Oct. 2025, doi: 10.33093/jiwe.2025.4.3.4.
[11] O. Baker, K. Subaramaniam, A. S. B. Shibghatullah, Z. A. Shaffiei, and A. S. S. S. Amir Hamzah, “A collaborative framework for disease prediction using machine learning,” in Proc. IEEE Int. Conf. Artif. Intell. Eng. Technol. (IICAIET), 2025, pp. 650–654, doi: 10.1109/IICAIET67254.2025.11265309.
[12] V. K. Damera, R. Cheripelli, N. Putta, G. Sirisha, and D. Kalavala, “Enhancing remote patient monitoring with AI-driven IoMT and cloud computing technologies,” Sci. Rep., vol. 15, no. 1, p. 24088, 2025, doi: 10.1038/s41598-025-09727-z.
[13] G. Sannino, A. Celesti, and I. De Falco, “Special Issue: ‘Intelligent Systems for Clinical Care and Remote Patient Monitoring’,” Sensors, vol. 23, no. 18, p. 7993, 2023, doi: 10.3390/s23187993.
[14] K. Perez, D. Wisniewski, A. Ari, K. Lee, C. Lieneck, and Z. Ramamonjiarivelo, “Investigation into application of AI and telemedicine in rural communities: A systematic literature review,” Healthcare, vol. 13, no. 3, p. 324, 2025, doi: 10.3390/healthcare13030324.
[15] S. Y. Kadum, O. H. Salman, Z. K. Taha, A. B. Said, M. A. M. Ali, Q. S. Qassim, M. I. Aal-Nouman, D. Y. Mohammed, B. M. Al Baker, and Z. A. Abdalkareem, “Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services,” Netw. Model. Anal. Health Inform. Bioinform., vol. 12, no. 1, p. 11, 2023, doi: 10.1007/s13721-022-00407-w.
[16] H. Faris, M. Habib, M. Faris, H. Elayan, and A. Alomari, “An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine,” Inform. Med. Unlocked, vol. 23, p. 100513, 2021, doi: 10.1016/j.imu.2021.100513.
[17] M. Awais, S. S. Fatima, and J. Iqbal, “Review on secure and efficient IoT-based healthcare system with the integration of machine learning and firewalls,” J. Inform. Web Eng., vol. 4, no. 3, pp. 140–152, Oct. 2025, doi: 10.33093/jiwe.2025.4.3.8.
[18] Palaniappan, S., Logeswaran, R., Subaramaniam, K., Baker, O., & Dung, B. N. (2026). Synthetic Data Generation for Healthcare Machine Learning: A Case Study in Vital Signs and Diagnostic Predictions. Journal of Informatics and Web Engineering, 5(2), 1–33. DOI: 10.33093/jiwe.2026.5.2.1.
[19] J. Wolber, A. Schuppert, M. Mücke, et al., “Predicting disease outcomes from remote monitoring using machine learning: A systematic review,” BMC Medical Informatics and Decision Making, vol. 26, art. 145, 2026.
doi: 10.1186/s12911-026-03495-0.
[20] M. Tsirintani, P. Lialiou, and P. Gallos, “The Impact of Telemedicine and Artificial Intelligence on Healthcare Quality,” Studies in Health Technology and Informatics, vol. 338, pp. 678–682, 2026.
doi: 10.3233/SHTI260931