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 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.