A Context-Aware AI Medical Assistant Using Retrieval-Augmented Large Language Models

Ajay Kumar Singh; Kalyanam  Bharat; Shyam  Baldua; Harshil  Jain

A Context-Aware AI Medical Assistant Using Retrieval-Augmented Large Language Models

Author(s)	Dr. Ajay Kumar Singh, Kalyanam Bharat, Shyam Baldua, Harshil Jain
Country	India
Abstract	This paper introduces a conversational medical assistance system that integrates a Large Language Model with retrieval-based grounding mechanisms to provide structured, non-diagnostic health guidance. Unlike conventional symptom checkers that rely on rigid decision trees, the proposed system dynamically interprets user queries, retrieves evidence-backed medical information from curated sources, and synthesizes contextual recommendations. The assistant generates medication suggestions, dosage information, safety precautions, lifestyle advice, and nearby pharmacy references. By incorporating Retrieval-Augmented Generation (RAG), the architecture reduces hallucinated responses and enhances factual alignment. Experimental validation across common minor conditions demonstrates improved contextual coherence and structured output reliability. The system is designed with a modular architecture that separates natural language processing, knowledge retrieval, and response generation layers to ensure scalability and maintainability. The retrieval module leverages indexed medical datasets and trusted clinical references to fetch relevant information in real time, while the language model refines this information into clear, user-friendly explanations. The interface supports conversational interaction, enabling users to ask follow-up questions and receive context-aware responses without repeating prior information. Furthermore, safety constraints are embedded within the system to prevent diagnostic claims and to encourage users to seek professional medical consultation when necessary. Performance evaluation metrics such as response accuracy, contextual relevance, and information completeness indicate that the integrated approach provides more reliable and interpretable outputs compared to standalone generative systems. This framework highlights the potential of combining modern AI language models with validated knowledge sources to develop responsible and practical digital health assistance tools. The proposed solution emphasizes interpretability and transparency as key design priorities, ensuring that users can easily understand the reasoning behind generated responses. By structuring outputs into clearly defined informational segments, the system minimizes ambiguity and promotes clarity in health-related communication. The conversational interface is also designed to accommodate natural language variations, allowing individuals to describe symptoms using everyday expressions without requiring technical medical vocabulary. This capability enhances accessibility for users from diverse backgrounds and improves overall usability of the platform. Moreover, the integration of retrieval-grounded generation establishes a balance between linguistic flexibility and factual reliability, which is essential in domains where informational accuracy is critical. The architecture demonstrates how modern AI systems can be adapted to provide supportive guidance while respecting safety boundaries and ethical considerations. As advancements in medical datasets, language modeling techniques, and retrieval optimization continue, such hybrid systems are expected to evolve into increasingly dependable digital tools capable of assisting users in making informed health-related decisions while complementing, rather than replacing, professional medical services.
Keywords	Healthcare AI, Large Language Models, Retrieval-Augmented Generation, Medical Recommendation System, Conversational NLP, Geolocation Services, Semantic Retrieval, Clinical Knowledge Integration, Intelligent Health Assistants, Context-Aware Systems, Natural Language Understanding, AI-driven Decision Support, Symptom Analysis Systems, Digital Health Platforms, Information Grounding, Medical Chatbots, Knowledge-Augmented AI, Health Informatics, Structured Response Generation, Patient Guidance Systems.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-26

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A Context-Aware AI Medical Assistant Using Retrieval-Augmented Large Language Models

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