AI-Powered Chatbot for FDA Drug Labeling Information Retrieval: OpenAI GPT for Grounded Question Answering

Abstract

This study presents the development of an AI-powered chatbot designed to facilitate accurate and efficient retrieval of information from the FDA drug labeling documents. Leveraging OpenAI’s GPT-3.5-turbo model within a controlled, document-grounded question–answering framework, Chatbot was created, which can provide users with answers that are strictly limited to the content of the uploaded drug label, thereby minimizing hallucinations and enhancing traceability. A user-friendly interface built with Streamlit allows users to upload FDA labeling PDFs and pose natural language queries. The chatbot extracts relevant sections using PyMuPDF and regex-based segmentation and generates responses constrained to those sections. To evaluate performance, semantic similarity scores were computed between generated answers and ground truth text using Sentence Transformers. Results across 10 breast cancer drug labels demonstrate high semantic alignment, with most scores ranging from 0.7 to 0.9, indicating reliable summarization and contextual fidelity. The chatbot achieved high semantic similarity scores (≥0.95 for concise sections) and ROUGE scores, confirming strong semantic and textual alignment. Comparative analysis with GPT-5-chat and NotebookLM demonstrated that our approach maintains accuracy and section-specific fidelity across models. The current work is limited to a small dataset, focused on breast cancer drugs. Future work will expand to diverse therapeutic areas and incorporate BERTScore and expert-based validation.

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