Blockchain and AI Integration for Secure Healthcare Data Management

References

[1] Tatineni, S. (2022). Integrating AI, Blockchain and cloud technologies for data management in healthcare. Journal of Computer Engineering and Technology (JCET), 5(01).

[2] Gummadi, J. C. S. (2022). Blockchain-Enabled Healthcare Systems: AI Integration for Improved Patient Data Privacy. Malaysian Journal of Medical and Biological Research, 9(2), 101-110.

[3] Duary, S., Choudhury, P., Mishra, S., Sharma, V., Rao, D. D., & Aderemi, A. P. (2024, February). Cybersecurity threats detection in intelligent networks using predictive analytics approaches. In 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1-5). IEEE.

[4] Yaqoob, I., Salah, K., Jayaraman, R., & Al-Hammadi, Y. (2022). Blockchain for healthcare data management: opportunities, challenges, and future recommendations. Neural Computing and Applications, 1-16.

[5] Daniel, R., Rao, D. D., Emerson Raja, J., Rao, D. C., & Deshpande, A. (2023). Optimizing routing in nature-inspired algorithms to improve performance of mobile ad-hoc network. International Journal of Intelligent Systems And Applications In Engineering, 11(8S), 508-516.

[6] Haddad, A., Habaebi, M. H., Islam, M. R., Hasbullah, N. F., & Zabidi, S. A. (2022). Systematic review on ai-blockchain based e-healthcare records management systems. IEEE access, 10, 94583-94615.

[7] Rajawat, A. S., Bedi, P., Goyal, S. B., Shaw, R. N., Ghosh, A., & Aggarwal, S. (2022). Ai and blockchain for healthcare data security in smart cities. AI and IoT for Smart City Applications, 185-198.

[8] Rao, D. D., & Sharma, S. (2023). Secure and Ethical Innovations: Patenting AI Models for Precision Medicine, Personalized Treatment and Drug Discovery in Healthcare. International Journal of Business, Management and Visuals (IJBMV), 6(2).

[9] Rao, D. D., Waoo, A. A., Singh, M. P., Pareek, P. K., Kamal, S., & Pandit, S. V. (2024). Strategizing IoT network layer security through advanced intrusion detection systems and AI-driven threat analysis. Full Length Article, 12(2), 195-195.

[10] Rao, D. D. (2009, November). Multimedia based intelligent content networking for future internet. In 2009 Third UKSim European Symposium on Computer Modeling and Simulation (pp. 55-59). IEEE.

[11] Ali, S., Abdullah, Armand, T. P. T., Athar, A., Hussain, A., Ali, M., ... & Kim, H. C. (2023). Metaverse in healthcare integrated with explainable AI and blockchain: enabling immersiveness, ensuring trust, and providing patient data security. Sensors, 23(2), 565.

[12] Sai, S., Chamola, V., Choo, K. K. R., Sikdar, B., & Rodrigues, J. J. (2022). Confluence of blockchain and artificial intelligence technologies for secure and scalable healthcare solutions: A review. IEEE Internet of Things Journal, 10(7), 5873-5897.

[13] Venkatesh, R., Rao, D. D., Sangeetha, V., Subbalakshmi, C., Bala Dhandayuthapani, V., & Mekala, R. (2024). Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis. International Journal of Applied and Computational Mathematics, 10(4), 130.

[14] Padmakala, S., Al-Farouni, M., Rao, D. D., Saritha, K., & Puneeth, R. P. (2024, August). Dynamic and Energy-Efficient Resource Allocation using Bat Optimization in 5G Cloud Radio Access Networks. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-4). IEEE.

[15] Rao, D. D., Jain, A., Sharma, S., Pandit, S. V., & Pandey, R. (2024). Effectual energy optimization stratagems for wireless sensor network collections through fuzzy-based inadequate clustering. SN Computer Science, 5(8), 1-10.

[16] Yadav, B., Rao, D. D., Mandiga, Y., Gill, N. S., Gulia, P., & Pareek, P. K. (2024). Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment. Journal of Cybersecurity & Information Management, 14(2).

[17] Alagarsundaram, P., Gattupalli, K., Gollavilli, V. S. B. H., Nagarajan, H., & Sitaraman, S.

[18] R. (2023). Integrating blockchain, AI, and machine learning for secure employee data management: Advanced control algorithms and sparse matrix techniques. Int J Comput Sci Eng Techniques, 7(1).

[19] Prabaharan, A. M. (2024). Advanced Data Integration for Smart Healthcare: Leveraging Blockchain and AI Technologies.

[20] Masarath, S., Waghmare, V. N., Kumar, S., Joshitta, R. S. M., & Rao, D. D. Storage Matched Systems for Single-click Photo Recognitions using CNN. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 1-7).

[21] Rao, D. D., Madasu, S., Gunturu, S. R., D’britto, C., & Lopes, J. Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study. International Journal on Recent and Innovation Trends in Computing and Communication, 12.

[22] Thapliyal, A., Bhagavathi, P. S., Arunan, T., & Rao, D. D. (2009, January). Realizing zones using UPnP. In 2009 6th IEEE Consumer Communications and Networking Conference (pp. 1-5). IEEE.

[23] Rana, S. K., Rana, S. K., Nisar, K., Ag Ibrahim, A. A., Rana, A. K., Goyal, N., & Chawla,

[24] P. (2022). Blockchain technology and artificial intelligence based decentralized access control model to enable secure interoperability for healthcare. Sustainability, 14(15), 9471.

[25] Alagarsundaram, P., Gattupalli, K., Gollavilli, V. S. B. H., Nagarajan, H., & Sitaraman, S.

[26] R. (2023). Integrating blockchain, AI, and machine learning for secure employee data management: Advanced control algorithms and sparse matrix techniques. Int J Comput Sci Eng Techniques, 7(1).

[27] Kumar, S., Joshitta, R. S. M., Rao, D. D., Masarath, S., & Waghmare, V. N. (2023, November). Storage Matched Systems for Single-Click Photo Recognition Using CNN. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 1-7). IEEE.

[28] Mahmoud, A., Imam, A., Usman, B., Yusif, A., & Rao, D. (2024). A Review on the Humanoid Robot and its Impact. Journal homepage: https://gjrpublication. com/gjrecs, 4(06).

[29] Alabdeli, H., Rafi, S., Rao, D. D., & Nagendar, Y. (2024, April). Photovoltaic Power Forecasting Using Support Vector Machine and Adaptive Learning Factor Ant Colony Optimization. In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1-5). IEEE.

[30] Bairwa, A. K., Yadav, R., Rao, D. D., Naidu, K., HC, Y., & Sharma, S. (2024). Implications of Cyber-Physical Adversarial Attacks on Autonomous Systems. Int. J. Exp. Res. Rev, 46, 273-284.

[31] Singh, B., & Kaunert, C. (2024). Reinventing Artificial Intelligence and Blockchain for Preserving Medical Data. In Ethical Artificial Intelligence in Power Electronics (pp. 77- 91). CRC Press.

[32] Patel, N. A., Ingle, P. S., Patsamatla, S. K., Omotunde, H., & Ingole, B. S. (2024). Integration of Blockchain and AI for Enhancing Data Security in Healthcare: A Systematic Review. Library of Progress-Library Science, Information Technology & Computer, 44(3).

[33] Elhoseny, M., Rao, D. D., Veerasamy, B. D., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2024). Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance. International Journal of Computational Intelligence Systems, 17(1), 1-19.

[34] Rao, D. D., Dhabliya, D., Dhore, A., Sharma, M., Mahat, S. S., & Shah, A. S. (2024, June). Content Delivery Models for Distributed and Cooperative Media Algorithms in Mobile Networks. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.

[35] Rao, D. D., Bala Dhandayuthapani, V., Subbalakshmi, C., Singh, M. P., Shukla, P. K., & Pandit, S. V. (2024). An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice & Applications, 15(2).

[36] Dubey, P., Dubey, P., Iwendi, C., Biamba, C. N., & Rao, D. D. (2025). Enhanced IoT- Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach with Adaptive Algorithms. IEEE Access.

[37] Chandratreya, A., Dodde, S., Joshi, N., Rao, D. D., & Ramteke⁵, N. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.

[38] Patel, N. A., Ingle, P. S., Patsamatla, S. K., Omotunde, H., & Ingole, B. S. (2024). Integration of Blockchain and AI for Enhancing Data Security in Healthcare: A Systematic Review. Library of Progress-Library Science, Information Technology & Computer, 44(3).

[39] Wehbe, Y., Al Zaabi, M., & Svetinovic, D. (2018, November). Blockchain AI framework for healthcare records management: constrained goal model. In 2018 26th Telecommunications forum (TELFOR) (pp. 420-425). IEEE.

[40] Anand, M. R. The Integration of AI and Blockchain in Healthcare: Ensuring Data Security and Integrity.

[41] Taloba, A. I., Rayan, A., Elhadad, A., Abozeid, A., Shahin, O. R., & Abd El-Aziz, R. M. (2021). A framework for secure healthcare data management using blockchain technology. International Journal of Advanced Computer Science and Applications, 12(12).

@article{1703495,
      author = {Hassan Tanveer, Muhammad Faheem, Arbaz Haider Khan},
      title = {Blockchain and AI Integration for Secure Healthcare Data Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {12},
      pages = {410-422},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703495.pdf},
      abstract = {The digital face-off of the healthcare industry is filled with challenges and concerns over data security, privacy, and interoperability. Traditional healthcare data management relies on so-called centralized architectures, all of which pose risks for cybersecurity attacks, data breaches, and unauthorized access. The integrating Blockchain and AI would therefore be a disruptive solution for secure and intelligent management of healthcare data. Blockchains provide decentralization, immutability, and secure access control, while AI allows for efficient data analysis, fraud detection, and decision-making. This paper discusses blockchain and AI synergistically protecting the sensitive healthcare data. Blockchain provides tamper-proof record-keeping and smart contract-enabled access control systems to ensure that only authorized entities can have access to patient records. AI solutions carry the heavier burden of fraud detection, predictive analytics, and automated diagnosis, which also enhances operation efficiency and clinical outcomes. Furthermore, the integration of AI with Blockchain enables federated learning privacy-preserving techniques, where an ML model is trained on decentralized data but patient privacy is not compromised. A mixed-method approach is employed that looks at a comparative analysis of existing Blockchain-AI models in healthcare and case studies from real-world implementations. The results indicate that the integration of Blockchain and AI improves data integrity and security, thereby ensuring interoperability and minimizing vulnerabilities associated with conventional systems. Nonetheless, complexities such as scalability issues, regulatory compliance, and computational overhead await resolution to allow the practical implementation of Blockchain for healthcare. The study is relevant in this burgeoning field of secure healthcare data management, establishing the synergy of Blockchain and AI for the establishment of a trustable, decentralized, and intelligent system. Further research should concentrate on the scalability of Blockchain systems, the transparency of AI models, and the creation of a coherent regulatory framework to support large- scale implementations.},
      keywords = {Blockchain, Artificial Intelligence, Healthcare Data Security, Cybersecurity, Data Privacy, Machine Learning, Healthcare Informatics, Privacy-Preserving AI, Digital Identity, Secure Data Sharing, Anomaly Detection.},
      month = {June},
  }