AI Chatbot for Medical Diagnosis and Analysis: A Native Android Application with a Retrieval-Augmented Generation Backend

The increasing strain on global healthcare systems, characterized by long wait times and physician shortages, necessitates innovative solutions to improve patient access to preliminary medical guidance. This paper presents the design and implementation of an "AI Chatbot for Medical Diagnosis and Analysis," a mobile health application aimed at addressing these challenges. The system integrates a native Android application developed using Kotlin and Jetpack Compose with a Python-based Retrieval-Augmented Generation (RAG) backend powered by a Large Language Model (LLM). The RAG approach ensures factual grounding by retrieving verified information from a curated medical knowledge base, minimizing hallucinations and inaccuracies. This work contributes a practical, safety-conscious blueprint for developing reliable AI-driven health applications that enhance triage efficiency and patient education.