ZODIAC让大模型具备心脏病专家级诊断能力，可自动分析心电图并生成报告。
Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnostics
- 采用多智能体协作框架，融合多模态患者数据进行专业分析
- 在8项临床指标上优于GPT-4o、Llama-3.1-405B等主流模型
- 已集成至心电图设备，适合医疗AI落地与临床辅助诊断场景
大型语言模型（LLMs）在医疗领域取得显著进展，但在特定临床实践中的专业性仍存在明显差距，限制了其在真实诊断中的应用。本文提出ZODIAC，一个具备心脏病专家级专业性的LLM框架，旨在支持心脏病学诊断。ZODIAC通过提取患者数据中的临床相关特征、检测显著心律失常，并生成供心脏病医生审核和修正的初步报告。为实现心脏病专家级专业性，ZODIAC基于多智能体协作框架，处理多模态患者数据。每个LLM智能体均使用心脏病医生审定的真实患者数据进行微调，强化模型的专业性。ZODIAC经过独立心脏病医生的严格临床验证，评估涵盖8项指标，衡量临床有效性并解决安全问题。结果表明，ZODIAC在性能上超越行业领先模型，包括OpenAI的GPT-4o、Meta的Llama-3.1-405B、Google的Gemini-pro，以及医学专用模型如Microsoft的BioGPT。ZODIAC展示了专业化大模型在医疗领域的变革潜力，提供满足医疗实践严苛要求的领域特定解决方案。值得注意的是，ZODIAC已成功集成至心电图（ECG）设备中，体现了将大模型嵌入医疗器械（SaMD）的趋势。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated remarkable progress in healthcare. However, a significant gap remains regarding LLMs' professionalism in domain-specific clinical practices, limiting their application in real-world diagnostics. In this work, we introduce ZODIAC, an LLM-powered framework with cardiologist-level professionalism designed to engage LLMs in cardiological diagnostics. ZODIAC assists cardiologists by extracting clinically relevant characteristics from patient data, detecting significant arrhythmias, and generating preliminary reports for the review and refinement by cardiologists. To achieve cardiologist-level professionalism, ZODIAC is built on a multi-agent collaboration framework, enabling the processing of patient data across multiple modalities. Each LLM agent is fine-tuned using real-world patient data adjudicated by cardiologists, reinforcing the model's professionalism. ZODIAC undergoes rigorous clinical validation with independent cardiologists, evaluated across eight metrics that measure clinical effectiveness and address security concerns. Results show that ZODIAC outperforms industry-leading models, including OpenAI's GPT-4o, Meta's Llama-3.1-405B, and Google's Gemini-pro, as well as medical-specialist LLMs like Microsoft's BioGPT. ZODIAC demonstrates the transformative potential of specialized LLMs in healthcare by delivering domain-specific solutions that meet the stringent demands of medical practice. Notably, ZODIAC has been successfully integrated into electrocardiography (ECG) devices, exemplifying the growing trend of embedding LLMs into Software-as-Medical-Device (SaMD).
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