Telemedicine Enhanced with Quantum Machine Learning for Secure and Real-Time Medical Diagnosis


  • N. Indumathi Assistant Professor, Department of Computer Science and Applications, SRM Institute of Science and Technology, Ramapuram Campus, Chennai
  • Hamza Mohammed Ridha Al-Khafaji Biomedical Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Hillah 51001, Babil, Iraq
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Arun Pratap Srivastava Lloyd Institute of Engineering & Technology, Greater Noida
  • Neeraj Varshney Department of Computer Engineering and Applications, GLA University, Mathura
  • Navneet Kumar Lloyd Law College, Greater Noida
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu


Quantum Machine Learning, Telemedicine, Quantum Encryption, User Experience, Healthcare Security


In order to provide safe and timely medical diagnoses, this study investigates the integration of quantum machine learning (QML) into telemedicine. With a deductive approach and an interpretive philosophy, the study uses secondary data collection and a descriptive design. The study examines resource utilization, processing speed, and diagnostic accuracy to assess the impact of QML. For increased security, quantum methods of encryption are closely examined. Stakeholder viewpoints and user experiences are highlighted by interpretive insights. Ethical considerations and implementation challenges are revealed through critical analysis. Interdisciplinary cooperation, user-centered design, and continuous observation of quantum developments are emphasized in the recommendations. Subsequent research endeavors ought to enhance QML algorithms, investigate adaptable quantum encryption, and tackle ethical considerations.


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How to Cite

Indumathi, N. ., Ridha Al-Khafaji, H. M. ., Deepak, A. ., Srivastava, A. P. ., Varshney, N. ., Kumar, N. ., & Shrivastava, A. . (2024). Telemedicine Enhanced with Quantum Machine Learning for Secure and Real-Time Medical Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 195–201. Retrieved from



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