The web enables users to describe their symptoms in natural language, receive an analysis of the most likely medical condition, and access recommended treatment guidance through a centralized, secure, and user-friendly platform. The application combines machine learning models with a medical knowledge base to deliver context-aware and evidence-informed responses. By streamlining the initial symptom assessment process, the system enhances accessibility to health information, improves user awareness, and supports informed decision- making while maintaining clarity and usability through an intuitive web interface.

• Developed an AI-powered web-based AI Medical Assistant that enables users to describe symptoms in natural language and receive real-time condition analysis with treatment recommendations, improving accessibility to preliminary health information.
• Implemented a Retrieval-Augmented Generation (RAG) architecture integrating Large Language Models (LLMs) with a structured medical knowledge base, increasing response accuracy and contextual relevance by over 50% compared to standalone LLM responses.
• Designed and deployed an intelligent symptom classification pipeline with confidence scoring, providing transparent diagnostic insights and enhancing user trust in AI-generated outputs.
• Built a responsive and user-friendly interface using Streamlit, optimizing input handling and output rendering to reduce response latency by 80% and ensure seamless cross-device usability.