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In-context learning enables multimodal large language models to classify cancer pathology images
This scientific article, published online November 21, 2024, explores the application of in-context learning (ICL) with multimodal large language models (LLMs), specifically GPT-4V, for classifying cancer pathology images. The authors demonstrate that ICL can improve the accuracy of these models in medical image analysis, matching or surpassing specialized neural networks trained for specific tasks, and doing so with minimal data requirements. The research highlights GPT-4V's ability to classify tissue subtypes, colon polyps, and breast tumor detection in lymph nodes, suggesting a potential to democratize AI access for medical experts. Ultimately, the study advocates for the potential of generalist AI models to perform complex medical image processing, reducing the need for extensive retraining and specialized models in the future.
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By Enoch H. KangThis scientific article, published online November 21, 2024, explores the application of in-context learning (ICL) with multimodal large language models (LLMs), specifically GPT-4V, for classifying cancer pathology images. The authors demonstrate that ICL can improve the accuracy of these models in medical image analysis, matching or surpassing specialized neural networks trained for specific tasks, and doing so with minimal data requirements. The research highlights GPT-4V's ability to classify tissue subtypes, colon polyps, and breast tumor detection in lymph nodes, suggesting a potential to democratize AI access for medical experts. Ultimately, the study advocates for the potential of generalist AI models to perform complex medical image processing, reducing the need for extensive retraining and specialized models in the future.
keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Map