Large language model chatbot-based text-to-SQL application for database analyses in liver diseases and hepatology research.

OBJECTIVE

Large language models (LLMs) may improve analysis of structured clinical data by enabling natural language (NL) queries. We developed an interactive, PHI-compliant "Text2SQL" chatbot as a proof-of-concept for hepatocellular carcinoma (HCC) research.

MATERIALS AND

METHODS

Using the University of California, San Francisco's secure artificial intelligence platform, we configured the chatbot to translate NL queries into Structured Query Language (SQL), execute them, and return both code and results. Clinicians posed 30 questions, and outputs were compared with SQL written by a data scientist. We assessed prompt interpretation, SQL accuracy, output accuracy, and error types according to strict binary criteria.

RESULTS

Prompt interpretation accuracy was 73.3%, SQL accuracy ranged from 53% to 63%, and output accuracy from 53% to 63%. Frequent errors included group comparisons, ambiguous variables, and exploratory questions.

DISCUSSION: Text2SQL enables PHI-compliant interaction with clinical datasets without coding, though performance varies across query types.

CONCLUSION

LLM-powered Text2SQL offers a promising approach for clinician-friendly clinical data access.