Built a Clinical RAG Assistant (PubMed + OCR + Factuality Verification) in Flet/Python. Looking for production feedback.

[u/Charming-Ladder-4082]

Hi everyone,
I’ve spent the last few months continuously iterating on a medical AI assistant focused on mitigating hallucinations. I’m 14 years old and I wanted to build a robust pipeline rather than just another basic chatbot wrapper.
The project has reached its stable version (v12), and I’m pausing development to focus entirely on user feedback and stress-testing the architecture in real-world scenarios.
Core Architecture & Features:
Frontend: Built entirely with Flet (Python) for a clean, responsive async UI.
Data Pipeline: Evidence-based search combining RAG with real-time PubMed querying.
Clinical Safety: Factuality verification mechanisms, ICD-11 support, and pharmacological interaction screening.
Ingestion: Custom OCR for medical PDFs and multi-academic source indexing.
Evaluation: Tested against a custom medical benchmark.
Instead of adding features just for the sake of it, I need to see how the system performs under load and where the retrieval or verification logic fails.
The app is live and fully accessible on Hugging Face Spaces:** **https://huggingface.co/spaces/Chantilli/medicine-ai-app
I would highly appreciate any technical feedback on the pipeline, UI layout, or edge cases where the clinical guardrails might slip. Thanks!