Concept Explainer

Uses analogies to explain complex medical concepts in accessible terms.

Explains medical concepts using everyday analogies.

Features

• Analogy generation
• Concept simplification
• Multiple explanation levels
• Visual description support

Parameters

Parameter	Type	Default	Required	Description
--concept, -c	string	-	Yes	Medical concept to explain
--audience, -a	string	patient	No	Target audience (child, patient, student)
--list, -l	flag	-	No	List all available concepts
--output, -o	string	-	No	Output JSON file path

Usage

bash

# Explain thrombosis to a patient
python scripts/main.py --concept "thrombosis"

# Explain to a child
python scripts/main.py --concept "immune system" --audience child

# Explain to a medical student
python scripts/main.py --concept "antibiotic resistance" --audience student

# List all available concepts
python scripts/main.py --list

Output Format

json

{
  "explanation": "string",
  "analogy": "string",
  "key_points": ["string"]
}

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support