ClinIQ: An AI-Based Clinical Decision Support System using Multi-Agent Reasoning, Retrieval-Augmented Generation, and Knowledge Graphs

Clinical decision-making requires clinicians to consider patient history, symptoms, laboratory findings, medications, and supporting medical documents. Reviewing these sources can be demanding, particularly when information is distributed across multiple records. Large language models (LLMs) offer opportunities to assist with clinical information synthesis, but their outputs may be incomplete, insufficiently grounded, or difficult to verify. This paper presents ClinIQ, an AI-based clinical decision support system that integrates retrieval-augmented generation (RAG), knowledge graph reasoning, and a multi-agent analysis workflow. The system combines structured patient information and user-provided clinical findings with relevant retrieved context to generate draft differential diagnoses, recommendations, and risk observations. Meta Llama 3 70B Instruct is accessed through Amazon Bedrock for language-based suggestion generation. The system also incorporates clinician review and decision recording to retain professional oversight. This paper describes the system architecture, implementation, data flow, and functional evaluation approach. ClinIQ is intended to support clinical information review rather than replace professional judgment; its clinical safety and effectiveness require further formal validation.