> For the complete documentation index, see [llms.txt](https://docs.getconduit.app/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.getconduit.app/use-cases/use-case-conduit-for-healthcare-applications.md).

# Use Case: Conduit for Healthcare Applications

#### Data Sharing with the AI

Conduit does not transmit actual data to AI models. With the traditional LLM systems like ChatGPT, you would send a CSV file containing data along with the question. However, with Conduit, the process is different and involves three key steps:

1. **Sending Metadata Description**: Conduit sends a description of the table's metadata, which includes the structure and types of data in the columns.
2. **Formulating the Question**: The question itself is sent to the AI.
3. **Requesting a Python Script**: A request is made for a Python script that will generate the answer.

In this way, Conduit only shares metadata, not the actual data, with the large language model (LLM).

**Controlled Access to Internal Data**

Moreover, access to internal data is stringently controlled within Conduit. Here’s how it ensures data security:

* **Authentication and Authorization**: Conduit uses robust authentication and authorization mechanisms to safeguard your internal data. It integrates with your data systems through SQL and APIs, ensuring that different users have different access levels.
* **User-Specific Data Retrieval**: When a user asks a question, the LLM generates a program to answer it. Conduit then provides the user's identity to your internal data API, which returns only the data that the user is authorized to see.
* **Executing the Program**: Conduit runs the generated program on the subset of data that the user is permitted to access, ensuring that the response is based on authorized data only.

**Mitigating Risks of Sharing Sensitive Information**

The system's design minimizes the risk of clients or patients sharing sensitive information. While this depends on specific use cases, the combined effect of the first two points ensures a setup that significantly reduces the potential for unauthorized access or exposure of sensitive information. By sharing only metadata and tightly controlling access to actual data, Conduit provides a secure and compliant environment for data interaction.