•  
  •  
 

Corresponding Author ORCID Identifer

Kasthuri Subaramaniam https://orcid.org/0000-0003-0704-923X
Oras Baker https://orcid.org/0000-0002-0958-4861

Section

Regular Issue

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.

Download

Share

COinS