Multimodal AI–Driven Real-Time Healthcare Analytics Using Leading Multicloud Platforms and Databricks
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Abstract
The increasing volume, velocity, and variety of healthcare data require advanced analytics frameworks capable of delivering real-time insights while supporting heterogeneous data modalities. This paper presents a multimodal AI–driven framework for real-time healthcare analytics using leading multicloud platforms integrated with Databricks. The proposed architecture enables seamless ingestion, processing, and analysis of structured, unstructured, imaging, and streaming healthcare data through unified data engineering and machine learning pipelines. Multimodal learning techniques are employed to fuse clinical records, medical images, sensor data, and textual information for enhanced predictive accuracy. Databricks serves as the core analytics and orchestration layer, facilitating scalable feature engineering, distributed training, and real-time inference across multicloud environments. The framework supports interoperability, fault tolerance, and elastic resource management, ensuring reliable performance for mission-critical healthcare applications. Experimental evaluation demonstrates improved latency, scalability, and analytical effectiveness compared to conventional single-cloud approaches. The results highlight the potential of multimodal AI and multicloud analytics in enabling intelligent, responsive, and data-driven healthcare systems.
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