AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications
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 multisou...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Al-Iraqia University - College of Engineering
2025-03-01
|
| Series: | Al-Iraqia Journal for Scientific Engineering Research |
| Subjects: | |
| Online Access: | https://ijser.aliraqia.edu.iq/index.php/ijser/article/view/294 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850233699095805952 |
|---|---|
| author | Sura Saad Mohsin Omar H. Salman Abdulrahman Ahmed Jasim Hajer Alwindawi Zahraa A. Abdalkareem Omar Sadeq Salman Ammar Riadh Kairaldeen |
| author_facet | Sura Saad Mohsin Omar H. Salman Abdulrahman Ahmed Jasim Hajer Alwindawi Zahraa A. Abdalkareem Omar Sadeq Salman Ammar Riadh Kairaldeen |
| author_sort | Sura Saad Mohsin |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-e8ec78951217476e9db86746c495f99e |
| institution | OA Journals |
| issn | 2710-2165 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Al-Iraqia University - College of Engineering |
| record_format | Article |
| series | Al-Iraqia Journal for Scientific Engineering Research |
| spelling | doaj-art-e8ec78951217476e9db86746c495f99e2025-08-20T02:02:51ZengAl-Iraqia University - College of EngineeringAl-Iraqia Journal for Scientific Engineering Research2710-21652025-03-014110.58564/IJSER.4.1.2025.294AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine ApplicationsSura Saad Mohsin0Omar H. Salman1Abdulrahman Ahmed Jasim2Hajer Alwindawi3Zahraa A. Abdalkareem4Omar Sadeq Salman5Ammar Riadh Kairaldeen6Dept. of Computer Engineering, Al-Iraqia University, IraqDept. of network and cyber security Engineering, Al-Iraqia University, IraqDept. of Electrical and Computer Engineering, Altinbas University, Istanbul, TurkeyDept. of Artificial Intelligence Engineering, Bahçeşehir University, Istanbul, TurkeyAlimam Aladham University College, Baghdad, IraqFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaComputer Science Department, UKM, Selangor, Malaysia 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. https://ijser.aliraqia.edu.iq/index.php/ijser/article/view/294Telemedicine, Artificial Intelligence (AI), Real-Time Triage, Rule-based algorithm, IoMT |
| spellingShingle | Sura Saad Mohsin Omar H. Salman Abdulrahman Ahmed Jasim Hajer Alwindawi Zahraa A. Abdalkareem Omar Sadeq Salman Ammar Riadh Kairaldeen AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications Al-Iraqia Journal for Scientific Engineering Research Telemedicine, Artificial Intelligence (AI), Real-Time Triage, Rule-based algorithm, IoMT |
| title | AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications |
| title_full | AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications |
| title_fullStr | AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications |
| title_full_unstemmed | AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications |
| title_short | AI-Powered IoMT Framework for Remote Triage and Diagnosis in Telemedicine Applications |
| title_sort | ai powered iomt framework for remote triage and diagnosis in telemedicine applications |
| topic | Telemedicine, Artificial Intelligence (AI), Real-Time Triage, Rule-based algorithm, IoMT |
| url | https://ijser.aliraqia.edu.iq/index.php/ijser/article/view/294 |
| work_keys_str_mv | AT surasaadmohsin aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT omarhsalman aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT abdulrahmanahmedjasim aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT hajeralwindawi aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT zahraaaabdalkareem aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT omarsadeqsalman aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications AT ammarriadhkairaldeen aipowerediomtframeworkforremotetriageanddiagnosisintelemedicineapplications |