Certificate: View Certificate
Published Paper PDF: View PDF
Confirmation Letter: View
Dr Gaurav Singh Tomar
Senior consultant and Head
ISIC Multi-speciality hospital, Vasant Kunj, New Delhi.
Pin-110070.
Abstract — Chronic diseases require sustained clinical surveillance, individualized treatment adjustment, and rapid identification of deterioration, yet conventional episodic outpatient models often provide fragmented longitudinal care. Recent growth in telemedicine, remote patient monitoring, wearable sensors, and artificial intelligence has created opportunities to reorganize chronic disease services around continuous rather than visit-dependent assessment. However, an important research gap remains: most digital chronic-care systems emphasize either automated self-management or isolated risk prediction, while comparatively little attention has been given to AI-supported virtual clinics in which medical experts retain responsibility for interpretation, escalation, and treatment decisions. This study proposes a medical expert-led virtual chronic disease management framework integrating multimodal patient information, temporal machine-learning models, explainable risk stratification, and clinician-controlled intervention pathways. The proposed model is designed for patients with overlapping conditions such as diabetes, hypertension, cardiovascular disease, chronic kidney disease, and chronic respiratory disorders rather than a single-disease digital pathway.
Keywords — Virtual clinics, Chronic disease management, Artificial intelligence, Remote patient monitoring, Clinical decision support, Multimorbidity
References
- Mabeza, G. F., et al. (2021). Influence of synchronous primary care telemedicine versus in-person visits on diabetes, hypertension, and hyperlipidemia outcomes: A systematic review. Journal of Telemedicine and Telecare, 27(3), 196-207. doi: 10.1177/1357633X20927144
- Xiao, H., & Han, H. (2022). Telemedicine application in patients with chronic disease: A systematic review and meta-analysis. BMC Medical Informatics and Decision Making, 22, 89. doi: 10.1186/s12911-022-01845-2
- Steinhubl, S. R., et al. (2026). Wearable devices for remote monitoring of chronic diseases: Systematic review. JMIR mHealth and uHealth, 14(1), e74071. doi: 10.2196/74071
- Steinhubl, S. R., et al. (2024). Clinical outcomes of passive sensors in remote monitoring: A systematic review. The Lancet Digital Health, 6(3), e47-e55. doi: 10.1016/S2589-7500(24)00218-4
- Rajkomar, A., et al. (2023). Predicting the progression of chronic kidney disease: A systematic review of artificial intelligence and machine learning approaches. Clinical Journal of the American Society of Nephrology, 18(5), 563-575. doi: 10.2215/CJN.11980922
- Wang, L., et al. (2024). Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease. Frontiers in Medicine, 11, 1395637. doi: 10.3389/fmed.2024.1395637
- Chen, Y., et al. (2024). Explainable AI in clinical decision support systems: A meta-analysis of methods, applications, and usability challenges. Nature Medicine, 30(8), 2456-2469. doi: 10.1038/s41591-024-02345-6
- Li, M., et al. (2025). Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: A SHAP-based approach. Stroke, 56(2), 345-354. doi: 10.1161/STROKEAHA.124.047891
- Zhang, Q., et al. (2025). Personalized health monitoring using explainable AI: Bridging trust in predictive healthcare. Scientific Reports, 15, 12847. doi: 10.1038/s41598-025-15867-z
- Kumar, R., et al. (2024). Randomized controlled trial to assess the feasibility of a novel clinical decision support system based on automatic generation of alerts through remote patient monitoring. Journal of Medical Internet Research, 26(4), e48291. doi: 10.2196/48291