Researchers have developed a feature-guided zero-shot framework utilizing large language models (LLMs) for early screening of chronic kidney disease (CKD). This approach bypasses the need for extensive labeled datasets or resource-intensive pathology tests by focusing on a compact, clinically relevant subset of readily available community-based features. Evaluations across multiple LLMs, including LLaMA-3, Qwen-3, Mistral, and GPT-4o-mini, demonstrated that using this selected feature set consistently improved performance and generalizability across different datasets and countries, suggesting LLMs can offer a practical complement to traditional ML methods for CKD screening. AI
IMPACT LLMs can be adapted for medical screening using minimal data, potentially improving accessibility in resource-limited settings.
RANK_REASON The cluster contains an academic paper detailing a novel methodology for using LLMs in a specific medical screening context.
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