Author(s):
Kaveri S. Loharkar, Suvarna S. Vadje, Unnati V. Kuwar
Email(s):
kaveriloharkar2004@gmail.com
DOI:
10.52711/0975-4377.2026.00014 
Address:
Kaveri S. Loharkar*, Suvarna S. Vadje, Unnati V. Kuwar
Bachelor of Pharmacy, Loknete Dr. J.D. Pawar College of Pharmacy, Manur, Kalwan, 423501
Affiliated to Savitribai Phule Pune University, Maharashtra, India.
*Corresponding Author
Published In:
Volume - 18,
Issue - 1,
Year - 2026
ABSTRACT:
Telemedicine has emerged as a transformative approach in modern healthcare, enabling remote consultation, diagnosis, and patient monitoring through advanced digital technologies. It effectively bridges the gap between healthcare providers and patients, especially in areas with limited medical infrastructure. The integration of Artificial Intelligence (AI) further enhances telemedicine by improving diagnostic accuracy, operational efficiency, and personalized treatment. AI-based technologies such as machine learning, predictive analytics, natural language processing, and image recognition allow clinicians to analyze large volumes of patient data, including medical images, historical records, and biosignals, facilitating early disease detection, timely clinical decisions, and tailored management of chronic conditions. This review highlights the role of AI-assisted telemedicine in managing chronic diseases such as cardiovascular disorders, diabetes, cancer, hypertension, dermatological ailments, and infectious conditions. It also discusses AI-powered digital applications including SkinVision, AI Dermatologist, Skinive, DiabTrend, Center Health, MySugr, Circadian AI, QuickVitals, Caare Heart AI, NanoHealth, PathAI, Tempus, PaigeAI, BlueDot, Qure.ai, and Aarogya Setu—that enable remote monitoring, early disease detection, and continuous patient care, particularly for diabetic patients. These innovations improve healthcare accessibility, reduce costs, accelerate diagnostics, and promote active patient engagement through teleconsultations and real-time data assessment. Despite their potential, AI-driven telemedicine systems face challenges, including data security, algorithmic bias, high implementation costs, low digital literacy, and dependence on stable internet connectivity. Addressing these issues through ethical guidelines, ongoing research, and professional capacity-building is crucial for sustainable adoption. In conclusion, the convergence of AI and telemedicine marks a significant milestone in healthcare, enhancing efficiency, inclusivity, and patient-centered care. Continued technological advancements and international collaboration can further drive AI-enabled telemedicine toward equitable, high-quality, and personalized healthcare worldwide.
Cite this article:
Kaveri S. Loharkar, Suvarna S. Vadje, Unnati V. Kuwar. Integration of Artificial Intelligence in Telemedicine: Advancing Diagnosis, Monitoring, and Management of Chronic Diseases. Research Journal of Pharmaceutical Dosage Forms and Technology.2026; 18(1):83-9. doi: 10.52711/0975-4377.2026.00014
Cite(Electronic):
Kaveri S. Loharkar, Suvarna S. Vadje, Unnati V. Kuwar. Integration of Artificial Intelligence in Telemedicine: Advancing Diagnosis, Monitoring, and Management of Chronic Diseases. Research Journal of Pharmaceutical Dosage Forms and Technology.2026; 18(1):83-9. doi: 10.52711/0975-4377.2026.00014 Available on: https://rjpdft.com/AbstractView.aspx?PID=2026-18-1-14