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Assessing Large Multimodal Models for One-Shot Learning and Interpretability in Biomedical Image Classification.

Wenpin Hou¹, Qi Liu¹, Huifang Ma²

¹Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York 10032, NY, USA.

Advanced Intelligent Systems (Weinheim an Der Bergstrasse, Germany)

|August 21, 2025

Summary

Large multimodal models (LMMs) show promise in biomedical image classification, offering improved one-shot learning and interpretability over traditional methods. These advanced AI models aid in analyzing tissues, cells, and diseases for research and diagnostics.

Keywords:

biomedical image image classification large language model large multimodal model machine learning

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Area of Science:

Biomedical image analysis
Artificial intelligence in medicine
Computational biology

Background:

Image classification is crucial for biomedical research and clinical diagnostics.
Traditional methods often require extensive datasets and lack interpretability.

Purpose of the Study:

To evaluate the efficacy of large multimodal models (LMMs) for biomedical image classification.
To compare LMMs against traditional single-modal approaches.

Main Methods:

Utilized large multimodal models (LMMs), such as GPT-4, for image classification tasks.
Applied LMMs to diverse biomedical image datasets including tissues, cell types, and disease status.

Main Results:

LMMs demonstrated strong performance in one-shot learning and generalization capabilities.
Text-driven classification and enhanced interpretability were observed with LMMs.
LMMs outperformed traditional methods that require large datasets.

Conclusions:

Large multimodal models represent a significant advancement in biomedical image classification.
LMMs offer a more interpretable and data-efficient alternative for biological research and clinical applications.