Data science through natural language with ChatGPT's Code Interpreter

Area of Science:

• Biomedical Research
• Data Science
• Artificial Intelligence

Background:

• Large language models (LLMs) demonstrate advanced text understanding and generation capabilities.
• ChatGPT's Code Interpreter integrates natural language interaction with code execution for data analysis.

Purpose of the Study:

• To explore the utility of LLMs, specifically ChatGPT with Code Interpreter, in simplifying biomedical data analysis workflows.
• To assess the potential of conversational AI in assisting researchers with tasks ranging from data loading to advanced model interpretation.

Main Methods:

• Utilized materials from a prior tutorial to perform data analysis via natural language prompts with ChatGPT.
• Covered key data science steps including data loading, exploration, model development, permutation importance, and partial dependence plots.

Main Results:

• Demonstrated that LLMs can effectively perform complex data analysis tasks through conversational interactions.
• LLM assistance allows researchers to concentrate on higher-level scientific inquiry and interpretation.

Conclusions:

• LLMs show significant promise in transforming data science workflows and supporting biomedical research.
• Critical considerations include ensuring responsible use, addressing privacy and security, and promoting equitable access to these AI tools.