arXiv 2511.086142025-11-15cs.CL

基于大语言模型的医疗急救顾问超级学习器

A Super-Learner with Large Language Models for Medical Emergency Advising

Northeastern University（东北大学）；Biovitalage S.R.L.（Biovitalage有限责任公司）；Territorial Emergency System SET 118（地区紧急系统SET 118）；Territorial Center for Medical Assistance（地区医疗援助中心）；Aldo Moro University of Bari（巴里阿尔多·莫罗大学）

Sergey K. Aityan, Abdolreza Mosaddegh, Rolando Herrero, Haitham Tayyar, Jiang Han, Vikram Sawant, Qi Chen, Rishabh Jain, Aruna Senthamaraikannan, Stephen Wood, … 展开作者

Sergey K. Aityan, Abdolreza Mosaddegh, Rolando Herrero, Haitham Tayyar, Jiang Han, Vikram Sawant, Qi Chen, Rishabh Jain, Aruna Senthamaraikannan, Stephen Wood, Manuel Mersini, Rita Lazzaro, Mario Balzanelli, Nicola Iacovazzo, Ciro Gargiulo Isacco

备注：: 12 pages, 3 figures, 2 tables

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出基于大语言模型的医疗急救顾问超级学习器，通过整合多个模型提升诊断准确率，达到70%甚至85%的高水平表现。

摘要：

医疗决策支持和建议系统对急诊医生快速准确评估患者病情和作出诊断至关重要。人工智能（AI）近年来在医疗领域已成为变革性力量，大语言模型（LLMs）已被应用于各种医疗决策支持系统中。我们研究了不同LLMs对急诊医学真实病例的响应。在对五种最著名的LLMs进行研究后，结果显示这些大语言模型在诊断急性疾病方面的能力存在显著差异，准确率在58%到65%之间。这种准确率显著超过了报道的人类医生的准确率。我们构建了一个超级学习器MEDAS（医疗急救诊断建议系统），整合了五种主要的LLMs——Gemini、Llama、Grok、GPT和Claude。超级学习器即使使用一个相当基础的元学习器，也能提供70%的诊断准确率。然而，至少有一个整合的LLM在同一超级学习器中能提供85%的正确诊断。超级学习器通过一个能够学习每个LLM不同能力的元学习器，整合一组LLMs，利用所有LLMs在集群中的集体能力来提高模型的诊断准确率。我们的研究表明，通过元学习方法提供的聚合诊断准确率超过了任何单个LLM的准确率，这表明超级学习器可以利用用于训练LLMs组的医疗数据集的综合知识。

英文摘要：

Medical decision-support and advising systems are critical for emergency physicians to quickly and accurately assess patients' conditions and make diagnosis. Artificial Intelligence (AI) has emerged as a transformative force in healthcare in recent years and Large Language Models (LLMs) have been employed in various fields of medical decision-support systems. We studied responses of a group of different LLMs to real cases in emergency medicine. The results of our study on five most renown LLMs showed significant differences in capabilities of Large Language Models for diagnostics acute diseases in medical emergencies with accuracy ranging between 58% and 65%. This accuracy significantly exceeds the reported accuracy of human doctors. We built a super-learner MEDAS (Medical Emergency Diagnostic Advising System) of five major LLMs - Gemini, Llama, Grok, GPT, and Claude). The super-learner produces higher diagnostic accuracy, 70%, even with a quite basic meta-learner. However, at least one of the integrated LLMs in the same super-learner produces 85% correct diagnoses. The super-learner integrates a cluster of LLMs using a meta-learner capable of learning different capabilities of each LLM to leverage diagnostic accuracy of the model by collective capabilities of all LLMs in the cluster. The results of our study showed that aggregated diagnostic accuracy provided by a meta-learning approach exceeds that of any individual LLM, suggesting that the super-learner can take advantage of the combined knowledge of the medical datasets used to train the group of LLMs.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。