> For the complete documentation index, see [llms.txt](https://docs.owkin.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owkin.com/explore-and-analyse-data/k-pro-data-model-and-technical-references/the-ai-maturity-model.md).

# The AI-maturity model

An AI-ready dataset is a collection of biomedical data specifically prepared so that both humans and K-Pro can seamlessly use it for analytics, model training and research. To establish a systematic way of assessing the value of a dataset, Owkin has introduced an AI-Readiness Maturity Model on a 6-level scale for its own datasets:

* **Level 0 - Uncontrolled data:** Data lacks governance, compliance, or minimal metadata for cataloging.
* **Level 1 - Storage & Legal compliance:** Raw data with minimal metadata; stored securely, ISO 27001 compliant, license & IRB in place.
* **Level 2 – Discoverability:** Data dictionary, schema, programmatic metadata access, and version history available.
* **Level 3 – Exploration:** Quality checks documented, summary tables provided, with manifest/ReadMe for dataset exploration.
* **Level 4 – Interoperability:** Standard formats, cross-modality links, automated QC, and full data lineage.
* **Level 5 – Full traceability + Optimized for AI/ML:** Optimized views, precomputed features, reproducibility (code + environment), and detailed audits.

In order for a third-party dataset to be computed by K-Pro, it has to meet some strict requirements (layout, schema, dictionary) picked amongst the ones above, and described in this document. Owkin is keen to support your journey to achieve this.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.owkin.com/explore-and-analyse-data/k-pro-data-model-and-technical-references/the-ai-maturity-model.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.