Applied Research Advances

2027: Online first section
Articles

Artificial Intelligence-enhanced Blockchain Telehealth Systems: Pioneering Secure and Intelligent Remote Healthcare Delivery

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  • Waseem Ahmad,
  • Sana Shahab,
  • Tauseef Ahmad,
  • Mohd Anjum,
  • Mohd Sadim,
  • Habib Figa Guye
Waseem Ahmad
Department of Computer Science and Engineering, Vishveshwarya Group of Institutions, Gautam Buddha Nagar 201314, Uttar Pradesh, India
Sana Shahab
College of Business Administration, Princess Nourah Bint Abdulrahman University, PO Box 84428, Riyadh 11671, Saudi Arabia
Tauseef Ahmad
Department of Information Technology, Rajkiya Engineering College, Azamgarh 276201, India
Mohd Anjum
Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India
Mohd Sadim
Computer Science & Engineering Department, KCC Institute of Technology and Management, Greater Noida, Uttar Pradesh 201306, India
Habib Figa Guye
Department of Information Science, College of Informatics, Bule Hora University, Oromia 144, Ethiopia

Published 2026-10-24

Keywords

  • Predictive Analytics,
  • Artificial Intelligence,
  • Blockchain,
  • Telehealth System Research,
  • Data Security,
  • Gradient Boosting Machine
    • ...More
    Less

Copyright (c) 2026 Waseem Ahmad, Sana Shahab, Tauseef Ahmad, Mohd Anjum, Mohd Sadim, Habib Figa Guye (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ahmad, W., Shahab, S., Ahmad, T., Anjum, M., Sadim, M., & Guye, H. F. (2026). Artificial Intelligence-enhanced Blockchain Telehealth Systems: Pioneering Secure and Intelligent Remote Healthcare Delivery. Applied Research Advances, 3(1), 1-27. https://doi.org/10.65069/ara31202738
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Abstract

The artificial intelligence (AI)-enhanced Blockchain telehealth framework proposes a new architecture that addresses remote healthcare delivery challenges through AI and Blockchain technologies, making services secure, intelligent, and transparent. Traditional telehealth delivery models face significant data security, privacy, and trust concerns. This study addresses these issues by applying AI predictive analytics to assess service delivery risk among patient populations through patterns of telehealth utilization and accessibility across demographic, ethnic, and geographic cohorts. After collecting and preprocessing patient information from the Telehealth Services for Medicaid and CHIP Population dataset, a Gradient Boosting Machine model was developed, achieving 99.96 percent accuracy, 99.89 percent precision, 99.98 percent recall, and a 99.93 percent F1-score. The experimental evaluation showed promising performance, with very low gas consumption of about 22,500 units per transaction, a latency of 0.013 seconds, and a strong encryption throughput of 57.75 MB/s. These results confirm that the framework is scalable and highly efficient. Overall, the proposed framework would improve security, reliability, and interoperability in the telehealth ecosystem, supporting more robust, resilient, and patient-centered sustainable digital health initiatives.

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References

  1. Almadani, B., Kaisar, H., Thoker, I. R., & Aliyu, F. (2025). A systematic survey of distributed decision support systems in healthcare. Systems, 13(3), 157. https://doi.org/10.3390/systems13030157
  2. Chaparro-Cardenas, S. L., Ramirez-Bautista, J.-A., Terven, J., Cordova-Esparza, D.-M., Romero Gonzalez, J.-A., Ramırez-Pedraza, A., & Chavez-Urbiola, E. A. (2025). A technological review of digital twins and artificial intelligence for personalized and predictive healthcare. Healthcare, 13(14), 1763. https://doi.org/10.3390/healthcare13141763
  3. Pool, J., Akhlaghpour, S., Hassandoust, F., Fatehi, F., & Burton-Jones, A. (2025). Towards a model for understanding failures in health data protection: A mixed-methods study. Behaviour & Information Technology, 45(7), 1381–1406. https://doi.org/10.1080/0144929x.2025.2551568
  4. Karim, M. M., Van, D. H., Khan, S., Qu, Q., & Kholodov, Y. (2025). Ai agents meet blockchain: A survey on secure and scalable collaboration for multi-agents. Future Internet, 17(2), 57. https://doi.org/10.3390/fi17020057
  5. Pillay, T. S., Topcu, D. I., & Yenice, S. (2025). Harnessing ai for enhanced evidence-based laboratory medicine (eblm). Clinica Chimica Acta, 569, 120181. https://doi.org/10.1016/j.cca.2025.120181
  6. Cirillo, F., De Santis, M., & Esposito, C. (2025). Applications of solid platform and federated learning for decentralized health data management. In Artificial intelligence techniques for analysing sensitive data in medical cyber-physical systems (pp. 95–111). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70775-9_6
  7. Nazir, A., Hussain, A., Singh, M., & Assad, A. (2024). Deep learning in medicine: Advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis. Multimedia Tools and Applications, 84(17), 17677–17740. https://doi.org/10.1007/s11042-024-19694-8
  8. Rathore, N., Kumari, A., Patel, M., Chudasama, A., Bhalani, D., Tanwar, S., & Alabdulatif, A. (2024). Synergy of ai and blockchain to secure electronic healthcare records. SECURITY AND PRIVACY, 8(1). https://doi.org/10.1002/spy2.463
  9. Alserhani, F. (2025). Intrusion detection and real-time adaptive security in medical iot using a cyber-physical system design. Sensors, 25(15), 4720. https://doi.org/10.3390/s25154720
  10. Kotb, Y., Oueida, S., Mostafa, N., & Ali, N. (2025). Online federated deep probabilistic learning-based smart healthcare on multi-cloud systems. IEEE Access, 13, 75265–75293. https://doi.org/10.1109/access.2025.3557877
  11. Elhanashi, A., Saponara, S., Zheng, Q., Almutairi, N., Singh, Y., Kuanar, S., Ali, F., Unal, O., & Faghani, S. (2025). Ai-powered object detection in radiology: Current models, challenges, and future direction. Journal of Imaging, 11(5), 141. https://doi.org/10.3390/jimaging11050141
  12. Sayed, S., Famous, M. S., Mazumder, R., Khan, R. T., Kaiser, M. S., Hossain, M. S., Andersson, K., & Khondoker, R. (2025). Blockchain and interplanetary framework for decentralized and secure electronic health record management. Blockchains, 3(4), 12. https://doi.org/10.3390/blockchains3040012
  13. Yadegari, F., & Asosheh, A. (2025). A unified iot architectural model for smart hospitals: Enhancing interoperability, security, and efficiency through clinical information systems (cis). Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01197-4
  14. Adeleke, O. J., Jovanovich, K. D., Ogunbunmi, S., Samuel, O., & Kehinde, T. O. (2025). Comprehensive exploration of smart cities: A systematic review of benefits, challenges, and future directions in telecommunications and urban development. IEEE Sensors Reviews, 2, 228–245. https://doi.org/10.1109/sr.2025.3569239
  15. Dash, S. P. (2020). The impact of iot in healthcare: Global technological change & the roadmap to a networked architecture in india. Journal of the Indian Institute of Science, 100(4), 773–785. https://doi.org/10.1007/s41745-020-00208-y
  16. Chen, L., Zheng, X., Sun, Z., & Li, X. (2025). Study on the development of an intelligent regional health security monitoring and emergency medical response system in less developed regions. Journal of Computing and Electronic Information Management, 16(1), 74–82. https://doi.org/10.54097/ydgagy87
  17. Lakhwani, T. S., Sinjana, Y., & Kapoor, A. P. (2025). Beyond numbers: A novel iot and drone framework for enhancing healthcare logistics. Discover Health Systems, 4(1). https://doi.org/10.1007/s44250-025-00187-w
  18. Alharbi, S. H., Alzahrani, A. M., Syed, T. A., & Alqahtany, S. S. (2024). Integrity and privacy assurance framework for remote healthcare monitoring based on iot. Computers, 13(7), 164. https://doi.org/10.3390/computers13070164
  19. Shinde, R., Patil, S., Kotecha, K., Potdar, V., Selvachandran, G., & Abraham, A. (2023). Securing ai-based healthcare systems using blockchain technology: A state-of-the-art systematic literature review and future research directions. Transactions on Emerging Telecommunications Technologies, 35(1). https://doi.org/10.1002/ett.4884
  20. Saxena, S., Saxena, S., Tahilramani, N., & Charanarur, P. (2025). Blockchain enhanced smart healthcare management for chronic diseases. Discover Computing, 28(1). https://doi.org/10.1007/s10791-025-09574-6
  21. Pinto-Coelho, L. (2023). How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications. Bioengineering, 10(12), 1435. https://doi.org/10.3390/bioengineering10121435
  22. Rashid, Z., Ahmed, H., Nadeem, N., Zafar, S. B., & Yousaf, M. Z. (2025). The paradigm of digital health: Ai applications and transformative trends. Neural Computing and Applications, 37(17), 11039–11070. https://doi.org/10.1007/s00521-025-11081-0
  23. Saini, B., Singh, D., Sharma, K. C., & Saini, D. K. (2025). Ai-enhanced telemedicine: Transforming resource allocation and cost-efficiency analysis via advanced queueing model. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-15664-8
  24. Khamaj, A. (2025). Ai-enhanced chatbot for improving healthcare usability and accessibility for older adults. Alexandria Engineering Journal, 116, 202–213. https://doi.org/10.1016/j.aej.2024.12.090
  25. Huang, G., Chen, X., & Liao, C. (2025). Ai-driven wearable bioelectronics in digital healthcare. Biosensors, 15(7), 410. https://doi.org/10.3390/bios15070410
  26. Ghadi, Y. Y., Shah, S. F. A., Waheed, W., Mazhar, T., Ahmad, W., Saeed, M. M., & Hamam, H. (2025). Integration of wearable technology and artificial intelligence in digital health for remote patient care. Journal of Cloud Computing, 14(1). https://doi.org/10.1186/s13677-025-00759-4
  27. AlDhaen, F. S. (2025). Ai-powered transformation of healthcare: Enhancing patient safety through ai interventions with the mediating role of operational efficiency and moderating role of digital competence—insights from the gulf cooperation council region. Healthcare, 13(6), 614. https://doi.org/10.3390/healthcare13060614
  28. Moray, R. (2025). An investigation of factors impacting the acceptance of telehealth in rural india. Discover Social Science and Health, 5(1). https://doi.org/10.1007/s44155-025-00250-0
  29. Pokharel, B. P., Kshetri, N., Sharma, S. R., & Paudel, S. (2025). Blockhealthsecure: Integrating blockchain and cybersecurity in post-pandemic healthcare systems. Information, 16(2), 133. https://doi.org/10.3390/info16020133
  30. Barbaria, S., Jemai, A., Ceylan, H. I., Muntean, R. I., Dergaa, I., & Boussi Rahmouni, H. (2025). Advancing compliance with hipaa and gdpr in healthcare: A blockchain-based strategy for secure data exchange in clinical research involving private health information. Healthcare, 13(20), 2594. https://doi.org/10.3390/healthcare13202594
  31. Ewoh, P., Vartiainen, T., & Mantere, T. (2025). Sociotechnical cybersecurity framework for securing health care from vulnerabilities and cyberattacks: Scoping review. Journal of Medical Internet Research, 27, e75584. https://doi.org/10.2196/75584
  32. Chen, Q., & Sheng, N. (2024). A novel health monitoring system for vital signs using iot. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-69257-y
  33. Du, X., Chen, B., Ma, M., & Zhang, Y. (2021). Research on the application of blockchain in smart healthcare: Constructing a hierarchical framework (Y.-Z. Jiang, Ed.). Journal of Healthcare Engineering, 2021, 1–13. https://doi.org/10.1155/2021/6698122