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LLMs Enable Zero-Shot Chronic Kidney Disease Screening with Minimal Features

Muhammad Ashad Kabir, Sirajam Munira· July 15, 2026 View original

Key takeaways

  • LLMs can perform effective zero-shot chronic kidney disease screening using minimal, readily available patient features.
  • A feature-guided framework significantly improves LLM diagnostic accuracy without dataset-specific training.
  • This approach offers a practical, resource-efficient complement to conventional machine learning in healthcare.
  • The method shows promise for expanding early disease detection in resource-limited settings.

Who benefits

HealthcarePublic HealthMedical DiagnosticsPharma

Summary

This study explores using large language models for early-stage chronic kidney disease screening in a zero-shot setting, leveraging a small set of clinically meaningful features. The proposed feature-guided framework significantly improves accuracy across various LLMs and datasets, offering a practical complement to traditional machine learning methods.

Researchers have investigated the potential of large language models (LLMs) for early detection of chronic kidney disease (CKD) without requiring extensive, labeled datasets or complex pathology tests. Their novel approach, called a feature-guided zero-shot framework, focuses on using a compact set of easily accessible, clinically relevant patient features. This method serializes tabular patient data into text prompts, allowing LLMs to perform inference without specific training on the CKD datasets. The study evaluated four prominent LLMs (LLaMA-3, Qwen-3, Mistral, and GPT-4o-mini) across three diverse international CKD datasets. The results consistently showed that using the selected, minimal feature set led to statistically significant improvements in screening performance, achieving accuracy levels suitable for practical application. This suggests LLMs can provide a valuable, training-free tool for CKD screening, particularly in resource-limited environments, by focusing on readily available community-based information.

Why it matters

This research demonstrates a novel, resource-efficient way to apply LLMs in healthcare for early disease detection, potentially expanding access to screening in underserved areas. Professionals can explore zero-shot LLM applications for diagnostics where data labeling is costly or scarce.

How to implement this in your domain

  1. 1Identify specific diagnostic challenges in your domain that rely on complex data or extensive labeling.
  2. 2Curate a minimal, clinically relevant feature set from existing patient records or public health data.
  3. 3Develop standardized prompt templates to serialize tabular patient data into text for LLM input.
  4. 4Evaluate various LLMs for zero-shot inference performance on your specific diagnostic task.
  5. 5Pilot the LLM-based screening alongside traditional methods to assess real-world efficacy and integration.

Original post by Muhammad Ashad Kabir, Sirajam Munira

"arXiv:2607.12260v1 Announce Type: new Abstract: 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…"

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Originally posted by Muhammad Ashad Kabir, Sirajam Munira on X · view source

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