Context-Aware Multimodal Recommendation Framework for Personalized Healthcare Information Retrieval Using Large Language Models

Abstract

The rapid proliferation of large language models has catalyzed a transformation in healthcare information retrieval, yet most systems remain tethered to unimodal textual inputs and static relevance models that disregard temporal, situational, and individual patient contexts. This paper presents a context-aware multimodal recommendation framework that integrates clinical text, medical imaging, structured patient records, and environmental sensor streams into a unified retrieval and personalization pipeline orchestrated by large language models. Drawing upon system-level design principles, the framework employs modular encoders, cross-modal attention fusion, and adaptive context encoders to generate rich representations of user information needs. A dynamic personalization layer continuously refines recommendations by modeling longitudinal health profiles, activity patterns, and domain-specific preferences without retaining raw sensitive data. The architecture is evaluated through the lenses of scalability, infrastructure governance, fairness, robustness, and sustainability. We discuss structural trade-offs between centralized large-model inference and edge-distributed fine-tuning, the integration of federated learning with differential privacy, and the challenges of maintaining retrieval quality under evolving clinical knowledge. Policy implications surrounding algorithmic accountability, data sovereignty, and equitable access are examined, emphasizing that technical design choices directly shape socio-technical outcomes. The framework is positioned as a composite infrastructure that reconciles cutting-edge multimodal understanding with the stringent demands of healthcare ecosystems, offering a reference architecture for deployable, responsible, and personalized medical information systems.