AI Testing in Healthcare – Whitepaper

This repository contains the public whitepaper:

“Introducing AI Testing in Healthcare: Solving QA Challenges with Prompt Engineering”
✍️ Author: Pavan Kumar Pabbisetty

📄 About the Whitepaper

This whitepaper presents a new framework for testing AI systems in healthcare, focusing on:

• Shift-left QA using prompt engineering
• Semantic logging and audit trails for compliance
• Reusable prompt chains with embedded assertions
• AI-driven test case transformation (e.g., JAMA → Python)
• Future direction: ethical AI QA and red-team alignment

🔍 Why This Matters

Traditional QA methods fall short for generative AI systems, especially in safety-critical sectors like HealthTech. This paper outlines practical strategies and tools to:

• Improve coverage of AI outputs
• Automate repeatable validation logic
• Support traceability, compliance, and audit-readiness

📂 Repository Contents

whitepaper/
├── AI_Testing_in_Healthcare_Whitepaper_Public.md
├── AI_Testing_in_Healthcare_Whitepaper_Public.pdf
└── AI_Testing_in_Healthcare_Whitepaper_Public.docx

🧭 Future Roadmap

This repository may grow to include:

• Sample diagrams
• Prototypes of AgentTest
• Structured logging/test runners
• Prompt chain templates for QA teams

🌍 Vision & Alignment

This whitepaper and repository are part of a broader effort to reimagine quality assurance in the age of AI, with a focus on safe and efficient HealthTech systems.

The ideas documented here — such as prompt chaining, AI test automation, semantic logging, and ethical QA simulation — support emerging national goals in digital health, AI governance, and technology innovation.

This work is aligned with international innovation initiatives in:

• ✅ HealthTech safety and compliance
• ✅ AI safety, explainability, and reproducibility
• ✅ Next-generation tools for quality engineering in large language models (LLMs)

I aim to expand this repository with:

• Modular tools like AgentTest (prototype-in-progress)
• Public prompt libraries for QA use cases
• Visualizations of semantic QA workflows
• Test conversion helpers (manual → Python)

If you’re building in these areas or would like to collaborate, feel free to connect on LinkedIn

📜 License

MIT License or Creative Commons BY-SA 4.0 (depending on your preference)

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For feedback or collaboration:
📬 Reach out via LinkedIn → https://(www.linkedin.com/in/pavanaitestops/