RAG SYSTEMS & KNOWLEDGE INTELLIGENCE: UNLOCKING REAL-TIME CLINICAL DECISION SUPPORT

The growing adoption of large language models in healthcare has highlighted persistent challenges related to hallucinations, lack of traceability, and limited clinical trust. Retrieval-Augmented Generation (RAG) has emerged as a critical architectural advancement that integrates generative models with curated knowledge sources to support real-time, evidence-based clinical decision-making. This chapter conceptually examines how RAG systems enhance knowledge intelligence in healthcare by dynamically retrieving relevant medical literature, clinical guidelines, and patient-specific data before generating responses. By grounding outputs in verified sources, RAG architectures improve diagnostic reasoning, treatment recommendations, and clinical documentation while maintaining transparency and auditability. The chapter synthesizes recent developments in clinical decision support systems, knowledge graphs, vector databases, and retrieval pipelines to demonstrate how RAG reduces hallucinations and aligns AI outputs with medical evidence. It further explores RAG-enabled clinician workflows, including diagnostic support, drug–drug interaction checks, guideline adherence, and real-time summarization of electronic health records. Ethical, technical, and operational considerations such as data governance, latency, interoperability, and clinician oversight are also addressed. The chapter contributes a structured conceptual framework for deploying RAG-based clinical intelligence systems that balance automation with human expertise, offering insights for researchers, healthcare providers, and policymakers seeking safe and scalable AI integration in clinical environments.