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

> Source: <https://dev.to/beck_moulton/building-an-ai-pharmacist-detecting-drug-drug-interactions-with-rag-and-ocr-ncc>
> Published: 2026-08-20 00:17:00+00:00

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! 👇