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Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Zero-shot medical image classification via large multimodal models and knowledge graphs-driven processing.

Xinfu Liu1, Yirui Wu1, Yuting Zhou1

  • 1Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China; College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China.

Methods (San Diego, Calif.)
|October 15, 2025
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Summary
This summary is machine-generated.

This study introduces a Cross-Modal Knowledge Representation (CMKR) framework using large language models to improve medical image classification with unlabeled data. CMKR effectively extracts knowledge from images and text, outperforming existing methods.

Keywords:
Cross-modal alignmentKnowledge graphKnowledge representationLarge language modelMedical image classification

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Data Analysis
  • Natural Language Processing

Background:

  • Intelligent medical technologies have advanced, but handling vast, unlabeled medical diagnostic data remains a challenge.
  • Current methods struggle with specialized medical data, hindering accurate classification tasks.

Purpose of the Study:

  • To leverage large language models for processing massive unlabeled medical data.
  • To propose a novel framework for accurate medical image classification using unlabeled data.

Main Methods:

  • Developed a Cross-Modal Knowledge Representation (CMKR) framework utilizing large language models.
  • Extracted implicit knowledge from medical images and explicit textual knowledge using knowledge graphs.
  • Implemented a cross-modal alignment strategy to enhance intra- and inter-modal knowledge representation.

Main Results:

  • The proposed CMKR framework demonstrated superior performance in medical image classification tasks.
  • Extensive experiments on public datasets confirmed the method's effectiveness.
  • The approach successfully handled vast amounts of unlabeled medical data.

Conclusions:

  • The CMKR framework offers a promising solution for medical image classification with unlabeled data.
  • Leveraging large language models and cross-modal knowledge representation significantly improves accuracy.
  • This method advances the application of AI in analyzing complex medical data.