A Unified Cloud-Based Multi-Modal Explainable AI System for Healthcare Insights, Secure Business Analytics, Fraud Detection, and Pharmaceutical Network Analysis
DOI:
https://doi.org/10.15662/IJEETR.2025.0706019Keywords:
Cloud computing, Explainable AI, Multi-modal learning, Healthcare analytics, Secure business analytics, Fraud detection, Pharmaceutical network analysis, Graph analytics, Machine learning, Data securityAbstract
The rapid expansion of digital ecosystems in healthcare, business operations, and the pharmaceutical sector has increased the need for secure, transparent, and scalable analytical frameworks. This paper proposes a unified cloud-based multi-modal Explainable Artificial Intelligence (XAI) system designed to integrate heterogeneous data streams for comprehensive decision support. The proposed architecture leverages multi-modal learning to process structured, unstructured, and real-time data while employing XAI techniques to ensure interpretability, regulatory compliance, and trustworthiness across high-stakes environments.Within healthcare, the system enhances clinical decision-making, risk stratification, and patient outcome prediction by synthesizing medical records, imaging, sensor data, and population health datasets. In business and financial domains, the framework supports secure analytics through anomaly detection, behavioral modeling, and real-time risk scoring. For fraud detection, the system employs deep learning–based graph analysis and temporal modeling to identify complex fraudulent patterns with high accuracy. In pharmaceutical network analysis, it integrates molecular, biological, and market-level data to uncover drug–target interactions, optimize R&D workflows, and strengthen supply-chain surveillance.
By deploying the model on a cloud infrastructure, the framework ensures scalability, low-latency processing, secure data exchange, and seamless integration with existing digital platforms. Experimental evaluations demonstrate that the unified system outperforms traditional single-modal and non-explainable models in accuracy, interpretability, and operational robustness. This work contributes a versatile, transparent, and domain-adaptive XAI system capable of supporting mission-critical analytics across healthcare, business intelligence, fraud prevention, and pharmaceutical research.
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