Architecting a Secure and Reliable Clinical AI Chatbot with LLMs

Like many of you, I’ve integrated ChatGPT into my daily workflow. It’s fantastic for brainstorming or quick fixes. However, I decided to take a hands-on approach to bridge the massive gap between using the chat interface manually and actually building reliable software on top of these models. Especially when dealing with sensitive clinical data where “copy-paste” isn’t an option. Developing a clinical AI chatbot might be a simple yet valuable attempt using OpenAI API. I thought it would be good to take a simple step so that it can be improved over time.

However, the stakes in the clinical domain are incredibly high, and “out-of-the-box” behavior simply isn’t enough:

• Safety & Ethics: Patient-facing agents require rigorous guardrails. We are dealing with sensitive inferences and Protected Health Information (PHI), where the operational limits must be strictly defined. The priority isn’t just managing liability; it is preventing real-world harm to users who rely on these systems.
• Standardization: Engineering in this space must respect the rigidity of Real World Data (RWD) standards. Whether mapping to ICD-10 diagnoses, CPT procedures, or LOINC lab codes, there is no room for “creative” hallucinations. We need deterministic, consistent normalization to ensure data integrity.

I decided to dig deeper into the engineering side of things. I wanted to understand not just how to send a prompt, but how to manage the “memory” of a conversation programmatically, how to control the randomness (temperature) for consistent results, and most importantly, how to secure API keys properly instead of leaving them in plain text.

What started as a few simple scripts evolved into a structured ClinicalChatbot module. I applied standard Object-Oriented patterns to encapsulate the complexity of these new GenAI tools, demonstrating that while the models are new, the principles of robust state management and error handling remain the same. And finally, a simple yet effective clinical AI chatbot emerged.

I’ve cleaned up, compiled, and organized these local experiments into a polished notebook, which I’ve just pushed to my personal GitHub repository. It’s essentially a “from scratch” guide on setting up a robust environment for AI projects. If you are also transitioning from “prompting” to “engineering,” you might find it useful. I’d love to hear your thoughts or how you handle state in your own projects!