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SparkMinds
SparkMinds

AI Agent -
Medical Test Scanning

Industry

AI

Team size

3 people

Type

Web App

Location

Asia

Timeline

2 weeks

Technologies

N8n, Pinecone, Elevenlabs, AWS, ChatGPT

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Demo Account
Email: test123@yopmail.com        Password: 123456@aA

Demo Account
Email: test123@yopmail.com
Password: 123456@aA

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Project Overview

AI for cancer diagnosis based on test results. This AI agent helps doctors scan patient records to detect early cancer indicators, enabling timely treatment.

Key Features

  • Medical Data Aggregation: Collects patient data from hospitals and healthcare organizations for AI processing.
  • Data Structuring & Analysis: Data analysts clean, aggregate, and prepare cancer-related datasets for AI model training.
  • AI Model Development & Training: Builds a three-layer AI model, trains it with structured medical data, and evaluates its diagnostic accuracy.
  • PDF Test Result Analysis via Telegram: Patients can upload cancer marker reports; AI bot analyzes PDFs to detect indicators of cancer.
  • Automated Diagnostic Feedback: AI scans blood tests, cell results, and general examination data to estimate cancer probability and provide instant diagnosis.
  • Detailed Report Generation: Users can download a PDF summary of the diagnostic result with analysis insights.

Challenges

  • 1. Low-Quality Input Documents: Documents are often blurry, skewed, or poorly captured, making it difficult for OCR to accurately recognize text.
  • 2. Variety of Document Formats (Invoices, Delivery Notes, Itemized Lists, etc.): Each logistics partner or client may use different templates, making it difficult for a single model to accurately extract data across all formats.
  • 3. Complex Table Extraction (Tabular Data): Tables containing product or cost information are often misread by OCR, resulting in broken rows or columns and inaccurate data.
  • 4. Imperfect Extracted Data – Needs Verification: AI cannot guarantee 100% accuracy, which can lead to system errors if outputs aren't verified.

Solutions

  • Apply image pre-processing techniques such as: Sharpening and contrast enhancement , automatic deskewing (straightening the image), use libraries like OpenCV or PIL to standardize images before feeding them into the OCR process.
  • Develop a template classification model to identify the document type before extraction. Allow customizable training for enterprise-specific document templates.
  • Utilize specialized models for table extraction, such as Amazon Textract AnalyzeDocument.
  • Implement a human-in-the-loop confirmation interface that displays AI results and allows quick manual corrections.
  • Use confidence scores to highlight fields that require user verification.
  • Log corrected data to retrain and improve the model over time.

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