Second OpinAIon - Medical Diagnosis System
Medical diagnosis assistant using causal inference and LLMs for comprehensive analysis
🏥 Advanced Medical Diagnosis with Causal Inference
Second OpinAIon is a sophisticated medical diagnosis system that leverages causal inference and large language models to provide comprehensive medical analysis, diagnosis, and treatment recommendations for healthcare professionals.
Key Features
🔬 Causal Inference Engine
- Causal Graph Analysis: Identifies and visualizes causal relationships between medical factors
- Counterfactual Reasoning: Evaluates alternative scenarios to strengthen diagnostic confidence
- Evidence-Based Diagnosis: Ranks potential diagnoses based on causal analysis
💊 Treatment Intelligence
- Treatment Categorization: Classifies treatments as causal, preventative, or symptomatic
- Patient-Specific Planning: Tailors treatment plans to individual patient needs
- Comprehensive Reporting: Generates detailed PDF reports for documentation
🤖 AI-Powered Analysis
- Multi-Stage Workflow: Systematic approach from factor extraction to final treatment plan
- Interactive Visualization: Dynamic causal graphs and treatment comparisons
- Natural Language Interface: Chat-based interaction with the AI assistant
Technical Architecture
Core Components
Medical Factor Extraction
# Identifies from patient cases:
- Symptoms and conditions
- Test results and vital signs
- Medical history factors
- Environmental influences
Causal Analysis Pipeline
- Establishes causal relationships (e.g., “Appendicitis → Right Lower Quadrant Pain”)
- Validates completeness of medical information
- Performs counterfactual analysis for alternative explanations
- Ranks diagnoses based on causal evidence
Treatment Planning System
- Causal Treatment: Addresses root causes
- Preventative Care: Prevents complications
- Symptomatic Management: Provides relief while treating causes
- Personalized Adjustments: Considers patient-specific factors
Workflow Stages
Initial Analysis
- Patient case entry
- Medical factor extraction
- Causal relationship mapping
Diagnosis Phase
- Information validation
- Counterfactual analysis
- Diagnosis generation
Treatment Planning
- Treatment identification
- Patient-specific tailoring
- Final plan generation
Technology Stack
- AI Framework: Azure OpenAI API for LLM capabilities
- Backend: Python with Streamlit for web interface
- Visualization: Interactive causal graph generation
- Documentation: Automated PDF report generation
- Analysis: Causal inference algorithms and counterfactual reasoning
Use Cases
Clinical Decision Support
- Assists healthcare professionals in complex diagnoses
- Provides second opinions with causal explanations
- Identifies potential missed diagnoses through counterfactual analysis
Medical Education
- Demonstrates causal reasoning in medical diagnosis
- Visualizes relationships between symptoms and conditions
- Provides comprehensive case analysis for learning
Documentation & Compliance
- Generates detailed medical reports
- Maintains audit trail of diagnostic reasoning
- Supports evidence-based medicine practices
Safety & Ethics
This system is designed as a decision support tool for healthcare professionals:
- Not intended to replace clinical judgment
- Requires medical expertise for interpretation
- Emphasizes transparency in causal reasoning
- Maintains patient privacy and data security