MediVerify

“SEE BEYOND THE PRESCRIPTION”

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Inspiration

Handwritten medical prescriptions can be difficult for patients to read and understand. Medicine names, dosage instructions, frequency, and other details may be unclear, especially when the prescription is handwritten.

This inspired us to build MediVerify, an AI-assisted prescription screening platform that converts a photographed or scanned prescription into a structured and easy-to-understand review.

What the Project Does

MediVerify allows users to upload a prescription image and receive an AI-assisted analysis of its contents.

The system extracts text from the prescription, identifies the medicines, and presents important details such as dosage, frequency, and duration. It also provides a confidence score to indicate how clearly the prescription was interpreted.

The results are organized into a simple prescription review containing:

  • Detected medicines and their details
  • Reading confidence for identified medicines
  • Extracted prescription text
  • Timing guidance for the medicines
  • Potential condition-related insights
  • Drug interaction warnings
  • Prescription safety information
  • A history of previously analyzed prescriptions

The goal is to make complex or difficult-to-read prescriptions easier to understand while keeping the final medical decision with qualified healthcare professionals.

How We Built It

MediVerify uses a multimodal AI workflow to process prescription images and convert them into structured information.

The workflow is:

Prescription Image → AI/Image Analysis → Text Extraction → Medicine Identification → Medication Analysis → Interaction Check → Structured Report

The application consists of a web-based frontend and a backend that processes prescription data and stores analysis history. The interface provides separate sections for uploading prescriptions, viewing analysis results, and reviewing previous prescriptions.

Key Features

📷 Prescription Image Analysis

Users can upload a photograph or scanned image of a prescription for analysis.

💊 Medicine Detection

The system identifies medicines from the prescription and displays information such as dosage, frequency, and duration.

🔍 Confidence-Based Results

Each detected medicine includes a reading confidence score, helping users understand when the prescription may have been difficult for the system to interpret.

⚠️ Interaction Warnings

The system checks the detected medicines for potential interactions using the available medication data and displays interaction warnings when applicable.

📋 Extracted Text

The prescription's extracted text is displayed separately so users can compare the AI interpretation with the original prescription.

🕒 Medication Timing Guide

The results organize the available prescription instructions into an easy-to-read timing guide.

📚 Prescription History

Previous analyses are stored so users can access their prescription reviews later.

Challenges

The major challenge was dealing with handwritten prescriptions, where handwriting quality, abbreviations, image quality, and different writing styles can make medicine identification difficult.

Another challenge was presenting AI-generated results in a way that is useful without creating false confidence. To address this, the application displays confidence information and clearly positions the system as an AI-assisted informational tool rather than a replacement for a doctor or pharmacist.

What We Learned

Through this project, we learned how multimodal AI can be applied to a real-world healthcare problem by combining image understanding, text extraction, medicine identification, and structured data analysis.

We also learned the importance of uncertainty handling in healthcare applications. Instead of treating every AI result as completely reliable, showing confidence and encouraging professional verification can make the system more responsible and transparent.

Future Scope

Future versions of MediVerify could improve handwritten prescription recognition, support multiple languages, expand the medication database, provide more detailed medication safety information, and add voice-based interaction to make prescription information more accessible.

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