A comparative study evaluated the efficacy of zero-shot multimodal large language models (LLMs) against Convolutional Neural Network (CNN) based models for classifying 12-lead ECG images. While LLMs like GPT-5.2, GPT-4.1, and Gemini-2.5 Pro could generate plausible ECG narratives, their zero-shot diagnostic capabilities performed at near-chance levels (ROC-AUC around 0.5). In contrast, a custom-developed physiology-aware CNN, LeadGroupECG, demonstrated stable and reliable discrimination, achieving ROC-AUC scores of 0.92-0.94 internally and 0.85-0.86 externally, highlighting the continued necessity of domain-specific architectures for clinical AI applications. AI
IMPACT Domain-specific CNN architectures remain essential for reliable AI-based ECG interpretation, as current zero-shot multimodal LLMs show limited diagnostic discrimination.
RANK_REASON The cluster reports on a comparative study published in a paper, evaluating the performance of LLMs and CNNs on a specific task (ECG classification). [lever_c_demoted from research: ic=1 ai=1.0]
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