AI Agent - Imaging Diagnosis

AI Agent -
Imaging Diagnosis

Industry

Team size

3 people

Type

Web App

Location

Asia

Timeline

2 weeks

Technologies

N8n, Pinecone, Elevenlabs, AWS, ChatGPT

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 Agent – Imaging diagnosis is an AI-powered diagnostic tool that analyzes skin images to detect fungal and other dermatological conditions. It collects and processes medical data from clinics, hospitals, and doctors to train and improve model accuracy. The system supports large-scale diagnosis, aids in interpreting lab results and clinical records, and assists both medical and aesthetic professionals.

Key Features

Skin Image Recognition: Detects fungal and dermatological conditions through AI analysis of skin photos.
Medical Data Collection & Training: Aggregates patient data from clinics, hospitals, and doctors to build and refine AI models.
High-Volume Diagnosis Support: Enables rapid diagnosis across thousands of cases, aiding dermatologists in time-sensitive workflows.
Multi-Modal Medical Record Analysis: Supports reading and interpreting blood tests, X-rays, and cancer screening records for broader clinical insight.
Aesthetic & Clinical Consultation Support: Assists dermatology and aesthetic centers with image-based diagnostics and treatment recommendations.

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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