Cloud-Based Multimodal BERT Framework for Secure AI-Driven Healthcare and Financial Analytics: Advancing Data Privacy and Cross-Domain Learning
DOI:
https://doi.org/10.15662/IJARCST.2025.0806805Keywords:
Multimodal BERT, cloud computing; healthcare AI, data governance, privacy preservation, medical image processing, federated learning, differential privacy, transformer models, explainable AI, Azure, AWS, HIPAA compliance, audit assurance.Abstract
The convergence of healthcare and financial systems in the era of digital transformation demands advanced, privacy-preserving analytical frameworks capable of handling multimodal data. This paper presents a Cloud-Based Multimodal BERT Framework designed to integrate and analyze AI-driven healthcare and financial datasets securely. The proposed model leverages Bidirectional Encoder Representations from Transformers (BERT) enhanced with multimodal fusion techniques to process textual, numerical, and imaging data within a unified cloud environment. By embedding data governance and encryption mechanisms, the framework ensures compliance with healthcare and financial data privacy regulations such as HIPAA and GDPR. Cross-domain learning capabilities enable predictive insights across sectors—for instance, correlating medical outcomes with financial risk models—while maintaining strict access control and auditability. The system’s scalable cloud infrastructure supports real-time analytics and federated learning, minimizing data movement and exposure. Experimental evaluations demonstrate improved accuracy, interoperability, and privacy assurance compared to conventional isolated models. This research contributes to building trustworthy AI ecosystems that bridge healthcare intelligence with financial analytics through secure, compliant, and efficient data processing.
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