Open Access
UNLOCKING SYNERGIES: A FRAMEWORK FOR INTEGRATING ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGIES
4
Department of Computer Science, ETH Zurich, Switzerland
4
Department of Computer Science, ETH Zurich, Switzerland
Abstract
The confluence of Artificial Intelligence (AI) and Blockchain technologies represents a transformative frontier, promising unprecedented advancements across diverse sectors. While AI offers unparalleled capabilities in data analysis, prediction, and automation, its reliance on centralized data often raises concerns regarding integrity, privacy, and trust. Conversely, Blockchain provides decentralized, immutable, and transparent record-keeping, addressing critical trust and security issues. This article proposes a conceptual framework for understanding and leveraging the synergistic integration of AI and Blockchain. Drawing from an extensive review of contemporary literature, it delineates the mutual benefits, identifying how Blockchain can enhance AI's data integrity and security, and how AI can optimize Blockchain's efficiency and scalability. Furthermore, the article explores key applications and confronts the inherent challenges, including regulatory hurdles, technical complexities, and scalability limitations. The discussion emphasizes the profound implications of this convergence for future decentralized intelligent systems and outlines critical directions for future research and development.
Keywords
Artificial intelligence,
Blockchain,
Integration framework,
Distributed ledger
References
Bourazeri, M., Khatibi, S., & Sadeghi, A. (2021). Enhancing AI Data Integrity with Blockchain: A Systematic Review. Journal of Information Security and Applications, 58, 102868. doi:10.1016/j.jisa.2021.102868
Dorri, A., Kanhere, S. S., & Jurdak, R. (2019). Blockchain for IoT Security and Privacy: The Case Study of a Smart Home. IEEE Access, 7, 67912-67922. doi:10.1109/ACCESS.2019.2918358
Fang, Y., Liu, Y., Zhang, M., & Zhao, Y. (2020). Blockchain-Based Smart Contracts for Decentralized AI Systems: Challenges and Opportunities. IEEE Transactions on Network and Service Management, 17(2), 1046-1058. doi:10.1109/TNSM.2020.2995476
Hsieh, H. P., Li, W., & Lin, C. Y. (2020). Legal and Regulatory Challenges of Blockchain Technology in AI Systems. Computer Law & Security Review, 36(5), 105391. doi:10.1016/j.clsr.2020.105391
Huh, J., Lee, S., & Kim, H. (2017). Blockchain-Based Secure Data Sharing Framework for Healthcare Services. IEEE Access, 5, 26467-26478. doi:10.1109/ACCESS.2017.2787624
Iansiti, M., & Lakhani, K. R. (2017). The Truth About Blockchain. Harvard Business Review, 95(1), 118-127.
Kshetri, N. (2020). Blockchain's Roles in Meeting Key Supply Chain Management Objectives. International Journal of Information Management, 50, 42-51. doi:10.1016/j.ijinfomgt.2019.04.013
Mengelkamp, E., Gärttner, J., Böhme, R., & Weinhardt, C. (2018). A blockchain-based approach for efficient electricity trading. Energy, 120, 416-430. doi:10.1016/j.energy.2016.12.092
Narayanan, A., Bonneau, J., Felten, E., & Narayanan, A. (2016). Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press.
Zheng, Z., Xie, S., Dai, H. N., & Wang, H. (2019). Blockchain Challenges and Opportunities: A Survey. International Journal of Web and Grid Services, 15(1), 1-14. doi:10.1504/IJWGS.2019.099440
Zhou, X., Liu, Q., Zhang, W., & Wei, C. (2021). Blockchain-Based Data Management for Smart Supply Chain. IEEE Transactions on Engineering Management, 68(1), 101-112. doi:10.1109/TEM.2019.2915828
Dip Bharatbhai Patel. (2025). Leveraging BI for Competitive Advantage: Case Studies from Tech Giants. Frontiers in Emerging Engineering & Technologies, 2(04), 15–21. Retrieved from https://irjernet.com/index.php/feet/article/view/166
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