ArticleAdvanced intelligent systems (Weinheim an der Bergstrasse, Germany)2025
Assessing Large Multimodal Models for One-Shot Learning and Interpretability in Biomedical Image Classification.
Article in Advanced intelligent systems (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Lense: Optimizing data preprocessing in single-cell omics using LLMs.bioRxiv : the preprint server for biology · 2026Article
- Lense: optimizing data preprocessing in single-cell omics using large language models.Briefings in bioinformatics · 2026Article
- Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow.Scientific data · 2026Article
- Benchmarking large language models for genomic knowledge with GeneTuring.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking large language models for genomic knowledge with GeneTuring.Briefings in bioinformatics · 2025Article
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5 authors.
Funding
Abstract
Image classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. It is demonstrated that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status. LMMs stand out from traditional single-modal classification approaches, which often require large training datasets and offer limited interpretability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.