English(EN) Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study

LLM在零样本心电图诊断方面表现不佳，CNN表现更优

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-22 05:59

一项比较研究评估了零样本多模态大型语言模型（LLM）与基于卷积神经网络（CNN）的模型在12导联心电图图像分类方面的有效性。尽管GPT-5.2、GPT-4.1和Gemini-2.5 Pro等LLM能够生成看似合理的心电图描述，但其零样本诊断能力接近随机水平（ROC-AUC约为0.5）。相比之下，一项自主开发的生理感知CNN，LeadGroupECG，展示了稳定可靠的区分能力，内部ROC-AUC得分达到0.92-0.94，外部达到0.85-0.86，这凸显了临床AI应用中领域特定架构的持续必要性。 AI

影响领域特定的CNN架构对于可靠的AI心电图解读仍然至关重要，因为当前的零样本多模态LLM在诊断区分方面表现有限。

排序理由该集群报告了一篇论文发表的比较研究，评估了LLM和CNN在特定任务（心电图分类）上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-22 05:59

面向ECG图像分类的生理感知CNN与零样本多模态大模型：一项比较研究

Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. However, ECG image understanding significantly differs from general images as it depends on precise waveform morphology, lead relati…

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面向ECG图像分类的生理感知CNN与零样本多模态大模型：一项比较研究

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