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Related Experiment Video

Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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

Summary

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 alignment Knowledge graph Knowledge representation Large language model Medical 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.