斯坦福团队提出LLM4CKD框架，用少量数据就能做肾病筛查，比传统方法更灵活。
研究评估了大型语言模型在零样本和少样本情境下用于慢性肾病(CKD)筛查的效果。LLM4CKD框架使用临床选择的表格特征和结构化提示模板，无需任务特定训练即可实现推理。在低数据设置下，LLM仅需少量示例即可匹敌或超越传统方法，但性能依赖于模型且稳定性随输入复杂度增加而降低。
LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening
Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches require labeled data and model training, limiting their use in real-world screening settings. This study evaluates the effectiveness of large language models (LLMs) for CKD screening under zero-shot and few-shot in-context learning settings and compares them with traditional ML and DL methods. We propose a framework that uses clinically selected tabular features and structured prompt templates to enable LLM-based inference without task-specific training. LLM performance is evaluated across multiple prompt styles, feature configurations, and data settings, and compared with standard ML, DL, and tabular foundation model (TFM) baselines, and existing CKD screening tools. The results show that LLMs can achieve competitive performance using only a small number of examples, often matching or outperforming traditional approaches in low-data settings. However, their performance remains model-dependent and less stable as input complexity increases. In contrast, ML, DL, and TFM models show more consistent improvement with larger training data. Overall, the findings highlight a trade-off between data efficiency and stability, suggesting that LLMs may serve as a flexible complementary approach for CKD screening when labeled data are limited.