USMLE Case Generator

Generate USMLE Step 1 and Step 2 CK style clinical cases for medical education and board exam preparation.

Features

• Step 1 Cases: Basic science concepts, pathophysiology, pharmacology
• Step 2 Cases: Clinical diagnosis, management, next best steps
• Complete Vignettes: History, physical exam, labs, imaging
• Multiple Choice Questions: Single best answer format
• Answer Explanations: Detailed rationale for learning

Usage

# Generate a Step 1 case (pathophysiology focus)
python scripts/main.py --step 1 --topic cardiology --difficulty medium

# Generate a Step 2 case (clinical management focus)
python scripts/main.py --step 2 --topic nephrology --include-diagnosis

# Generate case with specific conditions
python scripts/main.py --step 2 --condition "diabetic ketoacidosis" --format json

Parameters

Topics Covered

• Cardiology
• Pulmonology
• Gastroenterology
• Nephrology
• Endocrinology
• Hematology/Oncology
• Infectious Disease
• Neurology
• Psychiatry
• Musculoskeletal
• Dermatology
• Obstetrics/Gynecology
• Pediatrics
• Surgery

Case Structure

Each generated case includes:

1. Patient Demographics: Age, gender, relevant background
2. Chief Complaint: Presenting problem
3. History of Present Illness: Detailed symptom timeline
4. Past Medical History: Relevant comorbidities
5. Medications: Current drug regimen
6. Allergies: Drug/environmental allergies
7. Family History: Genetic conditions
8. Social History: Smoking, alcohol, occupation
9. Physical Examination: Vital signs, relevant findings
10. Laboratory Studies: CBC, CMP, specific markers
11. Imaging/Diagnostics: X-ray, CT, ECG, etc.
12. Question: USMLE-style multiple choice
13. Answer Options: 5 choices (A-E)
14. Correct Answer: With detailed explanation
15. Educational Objectives: Key learning points

Output Formats

Text Format (Default)

Plain text suitable for printing or reading.

JSON Format

Structured data for integration with applications.

Markdown Format

Formatted for documentation or web display.

Technical Difficulty

High - Requires medical knowledge validation and clinical accuracy.

⚠️ Manual Review Required: Generated cases should be reviewed by medical professionals before use in high-stakes educational settings.

References

• references/topics.json - Medical specialty taxonomy
• references/case_templates.json - Case structure templates
• references/usmle_patterns.md - USMLE question patterns
• references/conditions/ - Condition-specific case data

Example Output

Case: A 58-year-old male with chest pain

A 58-year-old man presents to the emergency department with 
crushing substernal chest pain radiating to his left arm, 
beginning 2 hours ago at rest...

[History, physical, labs, ECG findings...]

Question: What is the most appropriate next step in management?

A. Administer aspirin and nitroglycerin
B. Order CT pulmonary angiography
C. Perform immediate synchronized cardioversion
D. Start heparin drip and call cardiology
E. Discharge with outpatient stress test

Correct Answer: D
Explanation: [Detailed rationale...]

Safety & Limitations

• Cases are AI-generated and may contain inaccuracies
• Not a substitute for professional medical education
• Always verify clinical details with authoritative sources
• Intended for educational purposes only

Dependencies

• Python 3.8+
• No external API dependencies (template-based generation)
• Optional: LLM integration for case variation

Risk Assessment

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

# Python dependencies
pip install -r requirements.txt

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