Ayusure

Ayusure

AI-Powered Medical Report Analysis with Ayurveda & Insurance Integration

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Built at Code With DCG

Created on 16th March 2025

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Ayusure

Ayusure

AI-Powered Medical Report Analysis with Ayurveda & Insurance Integration

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The problem Ayusure solves

• Patients struggle to understand complex medical reports, leading to delayed treatment and missed early intervention.
• Ayurveda remains an underutilized treasure due to the lack of Al-driven integration with modern science.
• Meanwhile, insurance fraud drains 45,000 Cr annually, with false claims and misinterpreted diagnoses causing massive financial losses.

Challenges we ran into

  1. Complex Medical Report Parsing & Standardization
    Problem: Medical reports come in various formats (PDFs, scans, images, handwritten notes), with inconsistent terminologies and structures. Extracting accurate data and standardizing it for AI analysis was a huge challenge.
    How We Solved It: We implemented advanced OCR (Optical Character Recognition) combined with NLP (Natural Language Processing) models fine-tuned specifically for medical terminology. We also created a pre-processing pipeline to standardize extracted data before feeding it into our AI system.
  2. Ayurveda and Modern Science Integration
    Problem: Bridging the gap between traditional Ayurvedic knowledge and modern medical data was tricky. Ayurveda has a holistic approach, while modern medicine is more symptom- and diagnosis-driven, making it difficult to find common ground.
    How We Solved It: We collaborated with Ayurvedic practitioners to create a structured dataset and mapped Ayurvedic remedies to modern medical conditions based on symptoms and patient history. The AI model was trained on this hybrid dataset to offer balanced, personalized recommendations.
  3. Fraud Detection Model Bias & False Positives
    Problem: Initially, our fraud detection model produced too many false positives, flagging genuine claims as fraudulent. This posed a serious problem as it risked rejecting valid insurance claims.
    How We Solved It: We improved the model by incorporating feedback loops with medical experts, allowing for human-in-the-loop verification. Additionally, we added more diverse datasets and ran continuous model evaluation to improve precision and reduce false positives.

Technologies used

JavaScript

Python

Hough Transform

AI/ML

React.js

NLP

Discussion

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