Project Overview
This project tackles one of the most critical challenges in modern medicine: the early and accurate detection of cancer. Doctors and radiologists are often faced with an overwhelming volume of complex patient data, from lab results and PDFs to clinical records. This AI agent was developed as an intelligent diagnostic assistant to help medical professionals scan this data for subtle, early-stage cancer indicators that might otherwise be missed.
The system securely aggregates and structures anonymized patient data from hospitals to build a robust, cancer-focused dataset. A sophisticated multi-layer AI model is then trained on this data to recognize complex patterns. Doctors can interact with the agent via a secure interface (like a Telegram bot) to upload patient test results. The AI analyzes these documents in real-time, estimates the probability of cancer indicators, and provides immediate, actionable feedback. This tool acts as a crucial second set of eyes, empowering doctors to make more informed decisions and enabling more timely, life-saving treatment.
Application Showcase
Key Features
- Medical Data Aggregation: Collects patient data from hospitals for AI processing.
- Data Structuring & Analysis: Prepares cancer-related datasets for AI model training.
- AI Model Development: Builds a three-layer AI model and evaluates its diagnostic accuracy.
- PDF Test Result Analysis: AI bot analyzes PDFs uploaded via Telegram to detect cancer indicators.
- Automated Diagnostic Feedback: Scans tests to estimate cancer probability and provide instant diagnosis.
- Detailed Report Generation: Users can download a PDF summary of the diagnostic result.
Solutions
- Established standardized data-sharing protocols with hospitals and automated validation.
- Employed data analysts and preprocessing tools to ensure datasets are model-ready.
- Designed a multi-layer AI architecture with rigorous training and validation cycles.
- Integrated with Telegram to allow PDF uploads with OCR and data extraction.
- Automated real-time diagnostic feedback, estimating cancer risk from medical inputs.
- Generated comprehensive PDF reports summarizing AI findings and suggestions.