AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications

Sura Saad Mohsin; Omar H. Salman; Abdulrahman Ahmed Jasim; Hajer Alwindawi; Zahraa A.  Abdalkareem; Omar Sadeq Salman; Ammar Riadh Kairaldeen

doi:10.58564/IJSER.4.1.2025.294

Authors

Sura Saad Mohsin Dept. of Computer Engineering, Al-Iraqia University, Iraq
Omar H. Salman Dept. of network and cyber security Engineering, Al-Iraqia University, Iraq
Abdulrahman Ahmed Jasim Dept. of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
Hajer Alwindawi Dept. of Artificial Intelligence Engineering, Bahçeşehir University, Istanbul, Turkey
Zahraa A. Abdalkareem Alimam Aladham University College, Baghdad, Iraq
Omar Sadeq Salman Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
Ammar Riadh Kairaldeen Computer Science Department, UKM, Selangor, Malaysia

DOI:

https://doi.org/10.58564/IJSER.4.1.2025.294

Keywords:

Telemedicine, Artificial Intelligence (AI), Real-Time Triage, Rule-based algorithm, IoMT

Abstract

Telemedicine is revolutionising health care by enabling remote patient monitoring and diagnosis, which is critical for the management of such chronic diseases as those affecting the heart. Although improved, existing frameworks often focus narrowly on triage or diagnosis, not incorporating multisource data to offer a comprehensive assessment. The proposed AI-powered IoMT-based framework solves these limitations in real-time triaging and diagnosing patients with chronic heart disease. The system integrates sensory and non-sensory information through rule-based algorithms for assigning patients to five emergency categories and offers preliminary diagnosis with practical treatment recommendations. The evaluated system was tested on a dataset of 250 patients in a virtual application scenario, achieving an overall triage classification and diagnostic accuracy of 98.4%. The approach strengthens the capacity of Telemedicine to provide timely, accurate, and resource-effective healthcare, especially in under-resourced or remote settings. Future work will focus on incorporating more advanced AI methods, extending the framework to other chronic diseases, and more considerable real-life scenario validation.

References

[1] A. Ahmed Jasim, L. Rafea Hazim, H. Alwindawi, and O. Ata, “Optimizing Prediction of Cardiac Conditions Using Hyper-Adaboost-Integrated Machine Learning Models,” Journal for Scientific Engineering Research, vol. 3, no. 3, 2024, doi: 10.58564/IJSER.3.3.2024.220.

[2] S. Pal, S. C. Kumar, R. Rajeswari, and K. B. Swain, “Remote health assistance and automatic ambulance service,” 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2017 - Proceedings, no. August, pp. 264–267, 2017, doi: 10.1109/ICSTM.2017.8089165.

[3] U. K. Prodhan, M. Z. Rahman, I. Jahan, A. Abid, and M. Bellah, “Development of a portable telemedicine tool for remote diagnosis of telemedicine application,” Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, vol. 2017-Janua, pp. 287–292, 2017, doi: 10.1109/CCAA.2017.8229817.

[4] E. Ceuca, “Development of a Remote Patient Monitoring Device for the Detection of Fainting and Critical Condition,” Proceedings of the International Spring Seminar on Electronics Technology, vol. 2021-May, no. December 1918, pp. 1–4, 2021, doi: 10.1109/ISSE51996.2021.9467632.

[5] R. Rani and H. T. Patil, “Portable audiometer for detecting hearing disorder at an early stage for cancer patient,” International Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT 2016, pp. 119–123, 2017, doi: 10.1109/ICACDOT.2016.7877563.

[6] P. Rajendra Prasad, N. Narayan, S. Gayathri, and S. Ganna, “An efficient E-Health monitoring with smart dispensing system for remote areas,” 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 - Proceedings, pp. 2120–2124, 2018, doi: 10.1109/RTEICT42901.2018.9012480.

[7] O. H. Salman, Z. Taha, M. Q. Alsabah, Y. S. Hussein, A. S. Mohammed, and M. Aal-Nouman, “A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work,” Comput Methods Programs Biomed, vol. 209, p. 106357, 2021, doi: 10.1016/j.cmpb.2021.106357.

[8] A. Monte Soldado et al., “Implementation and evaluation of telemedicine in burn care: Study of clinical safety and technical feasibility in a single burn center,” Burns, vol. 46, no. 7, pp. 1668–1673, 2020, doi: 10.1016/j.burns.2020.04.027.

[9] P. Galván, M. Velázquez, R. Rivas, G. Benitez, A. Barrios, and E. Hilario, “Health diagnosis improvement in remote community health centers through telemedicine,” Medicine Access @ Point of Care, vol. 2, p. 239920261775310, 2018, doi: 10.1177/2399202617753101.

[10] E. Mehraeen et al., “Telemedicine technologies and applications in the era of COVID-19 pandemic: A systematic review,” Health Informatics J, vol. 29, no. 2, pp. 1–31, 2023, doi: 10.1177/14604582231167431.

[11] J. Ferrer et al., “New tele-diagnostic model using volume sweep imaging for rural areas,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2622–2625, 2017, doi: 10.1109/EMBC.2017.8037395.

[12] O. H. Salman, M. I. Aal-Nouman, and Z. K. Taha, “Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring syste,” J Biomed Inform, vol. 112, no. October, p. 103592, 2020, doi: 10.1016/j.jbi.2020.103592.

[13] O. H. Salman, M. F. A. Rasid, M. I. Saripan, and S. K. Subramaniam, “Multi-sources data fusion framework for remote triage prioritization in telehealth,” J Med Syst, vol. 38, no. 9, 2014, doi: 10.1007/s10916-014-0103-4.

[14] O. S. Salman, N. M. A. A. Latiff, S. H. S. Arifin, O. H. Salman, and F. T. Al-Dhief, “Internet of Medical Things Based Telemedicine Framework for Remote Patients Triage and Emergency Medical Services,” Conference Proceedings - 2022 IEEE 6th International Symposium on Telecommunication Technologies: Intelligent Connectivity for Sustainable World, ISTT 2022, no. November, pp. 33–37, 2022, doi: 10.1109/ISTT56288.2022.9966532.

[15] O. S. Salman, N. M. Abdul Latiff, O. H. Salman, and S. H. Syed Ariffin, “A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems,” Neural Comput Appl, vol. 36, no. 17, pp. 10109–10122, 2024, doi: 10.1007/s00521-024-09600-6.

[16] A. Ahmed Jasim, O. Ata, and O. Hussein Salman, “Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems,” Int J Med Inform, vol. 192, no. July, p. 105608, 2024, doi: 10.1016/j.ijmedinf.2024.105608.

[17] B. D. Parameshachari, K. L. Hemalatha, J. Rahebi, M. R. Baker, and B. P. Upendra Roy, “Health-Care Monitoring of Patient using CNN based Model in Internet of Things,” International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023, no. August, 2023, doi: 10.1109/ICAISC58445.2023.10200455.

[18] H. Javaid, S. Saleem, B. Wajid, and U. G. Khan, “Diagnose a Disease: A Fog Assisted Disease Diagnosis Framework with Bidirectional LSTM,” 2021 International Conference on Digital Futures and Transformative Technologies, ICoDT2 2021, 2021, doi: 10.1109/ICoDT252288.2021.9441475.

[19] S. Divakaran, L. Manukonda, N. Sravya, M. M. M, and P. Janani, “IOT C linic -I nternet based P atient Monitoring and Diagnosis System,” 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 2858–2862, 2017.

[20] A. Choudhury, R. Krishnan, A. Gupta, Y. Swathi, and C. Supriya, “Remote patient care monitoring system for rural healthcare,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, pp. 2593–2598, 2018, doi: 10.1109/ICECDS.2017.8389922.

[21] J. Jennifer, M. N. Marrison, J. Seetha, S. Sivakumar, and P. Sathish Saravanan, “DMMRA: Dynamic medical machine for remote areas,” International Conference on Power and Embedded Drive Control, ICPEDC 2017, pp. 467–471, 2017, doi: 10.1109/ICPEDC.2017.8081135.

[22] J. Abbasi, L. Alina, A. M. Abro, and B. Lal, “SEHAT: Smart E-Health App for Telediagnosis and first opinion,” IMTIC 2021 - 6th International Multi-Topic ICT Conference: AI Meets IoT: Towards Next Generation Digital Transformation, no. November 2021, pp. 7–13, 2021, doi: 10.1109/IMTIC53841.2021.9719812.

[23] F. Faisal and S. A. Hossain, “IoT based remote medical diagnosis system using NodeMCU,” 2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019, 2019, doi: 10.1109/SKIMA47702.2019.8982509.

[24] B. Bhattacharya, S. Mohapatra, A. P. Mukhopadhyay, and S. Sah, “Remote cardiovascular health monitoring system with auto-diagnosis,” Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019, pp. 1–5, 2019, doi: 10.1109/ViTECoN.2019.8899345.

[25] S. A. Siddiqui, A. Ahmad, and N. Fatima, “IoT-based disease prediction using machine learning,” Computers and Electrical Engineering, vol. 108, no. December 2021, p. 108675, 2023, doi: 10.1016/j.compeleceng.2023.108675.

[26] R. Ani, S. Krishna, N. Anju, A. M. Sona, and O. S. Deepa, “IoT based patient monitoring and diagnostic prediction tool using ensemble classifier,” 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, vol. 2017-Janua, pp. 1588–1593, 2017, doi: 10.1109/ICACCI.2017.8126068.

[27] I. Zagan, V. G. Gaitan, N. Iuga, and A. Brezulianu, “M-GreenCARDIO embedded system designed for out-of-hospital cardiac patients,” 2018 14th International Conference on Development and Application Systems, DAS 2018 - Proceedings, pp. 11–17, 2018, doi: 10.1109/DAAS.2018.8396063.

[28] M. Devarajan, V. Subramaniyaswamy, V. Vijayakumar, and L. Ravi, “Fog-assisted personalized healthcare-support system for remote patients with diabetes,” J Ambient Intell Humaniz Comput, vol. 10, no. 10, pp. 3747–3760, 2019, doi: 10.1007/s12652-019-01291-5.

[29] A. Croatti, M. Longoni, and S. Montagna, “Applying Telemedicine for Stroke Remote Diagnosis: The TeleStroke System,” Procedia Comput Sci, vol. 198, no. 2021, pp. 164–170, 2021, doi: 10.1016/j.procs.2021.12.224.

[30] F. M. Garcia, R. Moraleda, S. Schez-Sobrino, D. N. Monekosso, D. Vallejo, and C. Glez-Morcillo, “Health-5G: A Mixed Reality-Based System for Remote Medical Assistance in Emergency Situations,” IEEE Access, vol. 11, no. May, pp. 59016–59032, 2023, doi: 10.1109/ACCESS.2023.3285420.

[31] A. L. Ruscelli, G. Cecchetti, I. Barsanti, M. Manciulli, P. Paolini, and P. Castoldi, “A medical tele-tutoring system for the Emergency Service,” 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021, pp. 410–412, 2021, doi: 10.1109/PerComWorkshops51409.2021.9431030.

[32] S. M. K. Mbengue, O. Diallo, H. M. El Ndoye, J. J. P. C. Rodrigues, A. Neto, and J. Al-Muhtadi, “Internet of Medical Things : Remote diagnosis and monitoring application for diabetics,” 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, pp. 583–588, 2020, doi: 10.1109/IWCMC48107.2020.9148130.

[33] D. Cinay, H. A. Murat, and D. Savas, “Development of IoMT Device for Mobile Eye Examination Via Cloud-based TeleOphthalmology,” 2020 21st International Conference on Research and Education in Mechatronics, REM 2020, 2020, doi: 10.1109/REM49740.2020.9313903.

[34] C. Y. Su, H. Samani, C. Y. Yang, and O. N. Newton Fernando, “Doctor Robot with Physical Examination for Skin Disease Diagnosis and Telemedicine Application,” 2018 International Conference on System Science and Engineering, ICSSE 2018, pp. 1–6, 2018, doi: 10.1109/ICSSE.2018.8520218.

[35] L. A. S. M. Neto, R. Pequeno, C. Almeida, K. Galdino, F. Martins, and A. V. De Moura, “A method for intelligent support to medical diagnosis in emergency cardiac care,” Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, pp. 4587–4593, 2017, doi: 10.1109/IJCNN.2017.7966438.

[36] M. Doshi, M. Fafadia, S. Oza, A. Deshmukh, and S. Pistolwala, “Remote Diagnosis of Heart Disease Using Telemedicine,” 2019 International Conference on Nascent Technologies in Engineering (ICNTE), no. Icnte, pp. 1–5, 2019.

[37] P. Panindre, V. Gandhi, and S. Kumar, “Artificial Intelligence-based Remote Diagnosis of Sleep Apnea using Instantaneous Heart Rates,” in Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering, 2021, pp. 169–174. doi: 10.1109/Confluence51648.2021.9377149.

[38] S. A. Fattah, N. M. Rahman, A. Maksud, S. I. Foysal, and R. I. Chowdhury, “Stetho-phone : Low-cost Digital Stethoscope for Remote Personalized Healthcare,” 2017.

[39] O. H. Salman, A. A. Zaidan, B. B. Zaidan, Naserkalid, and M. Hashim, “Novel Methodology for Triage and Prioritizing Using ‘big Data’ Patients with Chronic Heart Diseases Through Telemedicine Environmental,” Int J Inf Technol Decis Mak, vol. 16, no. 5, pp. 1211–1245, 2017, doi: 10.1142/S0219622017500225.

[40] N. Kalid et al., “Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization ‘Large Scales Data’ Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology,” J Med Syst, vol. 42, no. 4, 2018, doi: 10.1007/s10916-018-0916-7.

[41] K. I. Mohammed et al., “A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated from Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method,” IEEE Access, vol. 8, pp. 91521–91530, 2020, doi: 10.1109/ACCESS.2020.2994746.

[42] R. A. Hamid, A. S. Albahri, O. S. Albahri, and A. A. Zaidan, Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases, vol. 13, no. 9. Springer Berlin Heidelberg, 2022. doi: 10.1007/s12652-021-03325-3.

[43] S. P. Chatrati et al., “Smart home health monitoring system for predicting type 2 diabetes and hypertension,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 3, pp. 862–870, 2022, doi: 10.1016/j.jksuci.2020.01.010.

[44] S. Y. Kadum et al., “Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 12, no. 1, 2023, doi: 10.1007/s13721-022-00407-w.

[45] C. L. Haase, K. T. Eriksen, S. Lopes, A. Satylganova, V. Schnecke, and P. McEwan, “Body mass index and risk of obesity-related conditions in a cohort of 2.9 million people: Evidence from a UK primary care database,” Obes Sci Pract, vol. 7, no. 2, pp. 137–147, 2021, doi: 10.1002/osp4.474.

[46] T. Turap, T. B. Merupakan, T. B. Lebih, and T. D. Turap, Intelligent Systems Reference Library,Volume 17. doi: 10.1007/978-3-642-21004-4.

[47] A. A.-B. 1College Zainab T. Al-Ars1*, “Iraq’s Major Infectious Disease Diagnosis Using A Fuzzy Rule-Based System,” International Journal of Engineering & Technology, vol. 7, no. 8, pp. 4943–4948, 2018, doi: 10.14419/ijet.v7i4.19473.