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

A developer built an AI Pharmacist Assistant that uses OCR to scan drug labels and retrieval-augmented generation (RAG) to detect drug-drug interactions. The system extracts drug names from images, cross-references a SQLite database of known interactions, and uses an LLM to generate safety summaries. The project demonstrates a practical application of AI in healthcare automation.

Ever looked at a pile of medicine bottles and wondered, "Is it actually safe to take these together?" Polypharmacy—the simultaneous use of multiple drugs—is a significant challenge in modern healthcare. Misunderstanding Drug-Drug Interactions DDI can lead to severe side effects or reduced efficacy. In this tutorial, we are building an AI Pharmacist Assistant , an automated engine that uses Optical Character Recognition OCR to scan drug labels and Retrieval-Augmented Generation RAG to cross-reference a drug database. By leveraging AI healthcare automation and sophisticated LLM reasoning , we can create a safety net that identifies potential contraindications in seconds. The system follows a linear pipeline: capturing raw image data, converting it to structured text, retrieving medical facts from a local SQLite-based knowledge base, and finally, using an LLM to reason about the interactions. php graph TD A Drug Packaging Image -- |Tesseract OCR| B Extract Drug Names B -- C{Search SQLite DB} C -- |Found Interaction Data| D Context Construction D -- E LLM Reasoning Engine E -- F Safety Report & Warnings C -- |Not Found| G Web Search/LLM General Knowledge G -- E To follow along, you'll need the following tech stack: First, we need to turn those pixels into text. We use pytesseract to handle the OCR process. python import pytesseract from PIL import Image def extract drug names image path : Pre-processing could be added here grayscale, thresholding text = pytesseract.image to string Image.open image path In a real scenario, use an LLM or Regex to pull specific active ingredients from the raw text print f"Detected Text: {text}" return text Example usage raw text = extract drug names "prescription bottle.png" RAG is only as good as its data. We’ll store known drug interactions in a SQLite database. This mimics a local "Source of Truth" to prevent LLM hallucinations. python import sqlite3 def setup database : conn = sqlite3.connect 'pharmacist assistant.db' cursor = conn.cursor Create a table for Drug-Drug Interactions cursor.execute ''' CREATE TABLE IF NOT EXISTS interactions drug a TEXT, drug b TEXT, severity TEXT, description TEXT ''' Sample data In production, import from DrugBank or similar interactions = 'Aspirin', 'Warfarin', 'High', 'Increased risk of bleeding.' , 'Simvastatin', 'Amiodarone', 'Moderate', 'Increased risk of muscle breakdown.' cursor.executemany 'INSERT INTO interactions VALUES ?,?,?,? ', interactions conn.commit return conn db conn = setup database Now, we combine the extracted drug names with the retrieved database records and feed them into a Large Language Model. python import openai def check for interactions drug list, db conn : cursor = db conn.cursor context bits = Simple cross-check logic for i, drug a in enumerate drug list : for drug b in drug list i+1: : cursor.execute "SELECT FROM interactions WHERE drug a=? AND drug b=? OR drug a=? AND drug b=? ", drug a, drug b, drug b, drug a result = cursor.fetchone if result: context bits.append f"ALERT: {result 0 } and {result 1 } - {result 2 } severity. {result 3 }" Pass the context to the LLM prompt = f""" You are a clinical pharmacist. Based on the following data: Drugs detected: {', '.join drug list } Known interactions: {'. '.join context bits if context bits else 'No direct matches in DB.'} Provide a concise safety summary for the patient. """ response = openai.ChatCompletion.create model="gpt-4o", messages= {"role": "system", "content": "You are a medical assistant."}, {"role": "user", "content": prompt} return response.choices 0 .message.content Example logic drugs = "Aspirin", "Warfarin" print check for interactions drugs, db conn While this "Learning in Public" project is a great start, building medical AI requires extreme precision. Handling edge cases like dosage, patient history, and multi-ingredient medications is crucial for a production-grade engine. 💡 Source of Inspiration: For more production-ready examples and advanced patterns in AI-driven automation, check out the deep-dive articles at. They cover everything from vector database optimization to building resilient AI agents. WellAlly Blog By combining Tesseract OCR for data capture and a RAG-based logic engine , we've built a functional prototype of an AI Pharmacist. This architecture minimizes the risk of LLM hallucinations by forcing the model to check a verified database before giving advice. What's next? Are you working on AI in healthcare? Let’s chat in the comments 👇