Feature-Guided Zero-Shot CKD Screening Using LLMs

Jul 15, 2026

A new study proposes a feature-guided zero-shot framework that leverages large language models (LLMs) for early chronic kidney disease (CKD) screening without the need for dataset-specific training. By selecting a compact set of clinically meaningful and readily available features, the researchers evaluated four LLMs across three heterogeneous CKD datasets from different countries. The models achieved consistent and statistically significant improvements in balanced accuracy and probability estimates, reaching performance levels suitable for screening purposes.

Why it matters: This work shows that LLMs can enable training-free, clinically meaningful CKD screening using minimal, community-accessible features, potentially improving access to screening in resource-limited settings.

Full story at: arXiv Machine Learning ↗