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LLMs show promise for zero-shot chronic kidney disease screening

Researchers have explored the use of large language models (LLMs) for zero-shot screening of chronic kidney disease (CKD). By serializing patient data into text and using a guided selection of clinically meaningful features, LLMs like LLaMA-3, Qwen-3, Mistral, and GPT-4o-mini demonstrated performance suitable for screening purposes. This approach offers a practical, training-free method for CKD detection using readily available community-based patient features, potentially complementing traditional machine learning techniques. AI

IMPACT LLMs can be leveraged for medical screening tasks with minimal training data, offering a new avenue for early disease detection.

RANK_REASON The item is a research paper detailing the application of LLMs to a specific medical screening task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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LLMs show promise for zero-shot chronic kidney disease screening

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The item is a research paper detailing the application of LLMs to a specific medical screening task. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models

    Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screening methods remain difficult to deploy in community and resource-limited screening settings due to their reliance on large labeled…