Tool-wielding language model-based agent offers conversational exploration of clinical tabular data
Abstract
Advancing evidence-based medicine requires integrating clinical expertise with data analysis. While clinicians contribute essential domain knowledge, applying modern data science methods often requires specialized training, creating a barrier to adoption. To bridge this gap, we developed ChatDA, an artificial intelligence agent enabling large language model-mediated conversational analysis of de-identified clinical tabular datasets. ChatDA empowers clinicians to extract meaningful insights efficiently and accurately, making data-driven clinical research more accessible and effective.
Fields of Study & Subjects
Artificial Intelligence in Healthcare and Education
Machine Learning in Healthcare
Digital Mental Health Interventions
Publication details
- Journal / source
- npj Artificial Intelligence
- Publisher
- Springer Science and Business Media LLC
- Volume
- 2
- Issue
- 1
- Publication date
- 9 February 2026
- Language
- EN