Interpreting Medical Records with Generative AI: A Patient-Facing Chat Application Using Google Gemini

In the age of digital health, patients are increasingly gaining access to their personal medical data, including lab test results, vital signs, and diagnostic reports. However, the technical and clinical complexity of this information often renders it difficult for non-medical users to interpret, potentially leading to confusion, misinformation, or anxiety. This research presents the development of an AI-powered web application that assists users in understanding their health data through natural language interaction. The system utilizes Google’s Gemini generative AI model to process textual medical information and provide context-aware answers to user queries. The application, developed using the Streamlit framework, supports both direct text input and file uploads in PDF or TXT format. Extracted data is sent to the Gemini API using a structured prompt design that frames the model as a medical data assistant. The model’s output is constrained with instructions to prioritize accuracy, avoid conjecture, and issue disclaimers discouraging self-diagnosis. Core components of the system include file parsing using PyPDF2, secure API key management via dotenv, asynchronous API interaction, and a chat-like user interface that maintains dialogue context using Streamlit’s session state. This project demonstrates a practical and ethical implementation of generative AI in consumer-facing health technology. By enhancing the accessibility and interpretability of medical records, the application empowers users to become more informed participants in their healthcare journey, while reinforcing the importance of professional consultation for clinical decisions