AI-Driven Big Data Analytics for Personalized Medicine in Healthcare: Integrating Federated Learning, Blockchain, and Quantum Computing
As Artificial Intelligence (AI) and Big Data continue to evolve at a rapid pace, personalised medicine in healthcare has undergone revolutionary changes. Despite these advancements, current AI-driven systems encounter substantial hurdles in security, scalability, and privacy, particularly when handling sensitive patient data across decentralised networks. This paper introduces an innovative AI-Driven Big Data Analytics Framework that incorporates Federated Learning, Blockchain technology, and Quantum Computing to enhance personalised medicine. The proposed architecture ensures robust data privacy, real-time analytics, and secure patient data sharing, leading to significant improvements in healthcare diagnostics, treatment planning, and prognosis prediction.
Authors
- Santos Kumar Das (ORCID: https://orcid.org/0000-0002-8788-6152)
- Somnath Mondal (ORCID: https://orcid.org/0000-0002-5657-8077)
- Rajesh Bose (ORCID: https://orcid.org/0000-0002-0967-455X)
- Su Golder (ORCID: https://orcid.org/0000-0002-8987-5211)
- Haraprasad Mondal (ORCID: https://orcid.org/0000-0002-2056-6875)
- Sharabani Sutradhar
Institutions
- Techno India University (IN)
- Deloitte (United States) (US)
- Wacker (United States) (US)
- Dibrugarh University (IN)
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/icaiqsa64000.2024.10882330
- Citations
- 9
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 4.76