BioPrep AI

Inspiration

Biomedical AI starts with high-quality data, but preparing that data is often slow, repetitive, and difficult. Researchers spend countless hours cleaning biosignals, enhancing medical images, selecting preprocessing techniques, validating results, and documenting every step before training a single model.

We wanted to build an intelligent assistant that automates this entire workflow while remaining transparent, reproducible, and trustworthy. Instead of replacing domain experts, BioPrep AI accelerates their work by generating validated preprocessing pipelines that can be inspected, tested, and deployed.

What it does

BioPrep AI transforms raw biosensor data into production-ready preprocessing pipelines.

Users simply upload a biosignal (EEG, ECG, EMG, etc.) or biomedical image and describe their objective in plain English. The platform then:

Audits the input data
Detects quality issues automatically
Generates a custom preprocessing pipeline using AI
Executes the generated code in a secure sandbox
Automatically retries if the pipeline fails
Computes before-and-after quality metrics
Explains the improvements
Generates a complete HTML report
Produces a Docker-ready deployment package

The platform is available through both a command-line interface and an intuitive Streamlit web application.

How we built it

BioPrep AI combines deterministic scientific computing with modern AI.

Our stack includes:

Python
OpenAI-compatible models (Codex/OpenAI API)
Streamlit
NumPy
SciPy
OpenCV
Pillow
PyTest
Docker

The workflow begins by profiling the uploaded data using deterministic signal processing or computer vision techniques. This profile is passed to an LLM, which generates a preprocessing pipeline constrained by trusted templates. Every generated pipeline is executed inside a sandbox, automatically tested, and repaired if necessary. Finally, quality metrics, reports, and deployment artifacts are generated automatically.

Challenges we ran into

The biggest challenge was making AI-generated code reliable enough for scientific workflows.

Instead of trusting generated code directly, we designed a self-healing execution system that validates every generated pipeline. If execution fails, the system retries using the error context before falling back to trusted preprocessing methods.

Another challenge was creating a unified architecture capable of handling both biosignals and biomedical images while producing consistent reports and outputs.

Accomplishments that we're proud of

Built a unified preprocessing platform for signals and images
Automated pipeline generation using AI
Implemented self-healing pipeline execution
Added automatic quality evaluation with before/after metrics
Generated explainable HTML reports
Created both a CLI and a modern Streamlit interface
Enabled Docker-ready deployment for reproducible research

What we learned

This project reinforced that AI performs best when combined with deterministic validation rather than being trusted blindly.

We also learned the importance of explainability and reproducibility in scientific software. Researchers need to understand why preprocessing improves data quality, not just receive cleaned outputs.