Building an AI Pharmacist: Detecting Drug-Drug Interactions with RAG and OCR

Polypharmacy, or the simultaneous use of multiple medications, poses significant challenges in modern healthcare due to the risk of drug-drug interactions (DDIs). Misunderstanding these interactions can result in severe side effects or reduced efficacy. In this tutorial, an AI Pharmacist Assistant is developed using Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) to detect potential DDIs quickly.

The system's architecture consists of a linear pipeline, starting with capturing raw image data of drug labels. This data is then converted to structured text using Tesseract OCR. Subsequently, the extracted drug names are searched in a local SQLite-based knowledge base to retrieve relevant interaction data. If no interaction data is found, the system performs a web search or relies on the LLM's general knowledge to provide a safety report and warnings.

To implement this, the following prerequisites are required: Python 3.10+, Tesseract OCR for text extraction from images, SQLite for storing curated drug interaction data, the RAG pattern for providing ground-truth medical data to the LLM, and the OpenAI SDK for the final reasoning step. The first step involves extracting drug names from images using OCR.

The second step establishes the knowledge base by creating a SQLite database and populating it with drug interaction data. Finally, the RAG logic and LLM reasoning combine the extracted drug names with the retrieved database records to generate concise safety summaries for patients.