# Clinical Ai Decision Support System

```
`pip install -r requirements.txt` installs all Python dependencies for the Clinical AI Decision Support System.
```


```
`cp .env.example .env` creates a local environment file that I then edit to add my `GROQ_API_KEY`.
```


```
`uvicorn api.main:app --reload` starts the FastAPI backend with live reload.
```


```
`streamlit run app.py` launches the Streamlit front‑end UI.
```


All predictions include **SHAP explanations** so clinicians can understand which clinical features influence the model output.

LLM explanations are supported by **retrieved clinical guideline evidence**, helping reduce hallucinations and improve reliability.

Clinicians remain responsible for reviewing predictions and explanations through a **human‑in‑the‑loop decision process**.

Generated reasoning passes through **validation layers** that enforce safety checks and clinical disclaimers.

- The system monitors:

This project is a **research and educational implementation** of an AI‑powered clinical decision support system.

I use these steps whenever I need to spin up the CDSS locally for experimentation or demo purposes.

- Prediction confidence
- Model calibration
- Data drift
- Pipeline reliability

The repository started 2025-12 and was last updated 2026-10.

Topics: ai, ai-system, clinicaldecisionsupport, explainable-ai, fastapi, generative-ai, healthcare-ai, llm, machine-learning, rag, shap, streamlit.

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From Nithin Gowda P's second brain at agentsocialx.com/nithin10