Project Overview
This project addresses the critical need for rapid, accessible, and accurate diagnostic support in dermatology and clinical settings. We developed the AI Agent – Imaging Diagnosis, an advanced computer vision platform designed to analyze medical images, focusing initially on dermatological conditions. It functions as an intelligent assistant for medical professionals, helping to identify potential fungal infections and other skin abnormalities from patient photos with high precision.
The system is built on a sophisticated data pipeline (powered by n8n and AWS) that securely collects and processes anonymized medical data—including images, lab results, and clinical records—from partner clinics and hospitals. This rich, multi-modal dataset is used to continuously train and refine the underlying AI models (like ChatGPT) for classification and analysis. The workflow allows professionals to upload an image and receive a preliminary analysis, including potential conditions and confidence scores, in real-time. This enables the system to support high-volume diagnosis and assist in interpreting a wide array of medical records, from blood tests to X-rays.
Application Showcase
Key Features
- Skin Image Recognition: Detects fungal and dermatological conditions through AI analysis of skin photos.
- Medical Data Collection & Training: Aggregates patient data to build and refine AI models.
- High-Volume Diagnosis Support: Enables rapid diagnosis across thousands of cases.
- Multi-Modal Record Analysis: Supports reading blood tests, X-rays, and cancer screening records.
- Aesthetic & Clinical Support: Assists with image-based diagnostics and treatment recommendations.
Solutions
- Leveraged advanced computer vision models trained on diverse datasets.
- Established secure data pipelines and anonymization protocols for clinical data.
- Optimized model inference on scalable infrastructure for real-time diagnosis.
- Integrated multi-modal AI models capable of parsing varied diagnostic formats.
- Provided clear diagnostic outputs with confidence scores and suggestions.