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GastroGPT:一个定制的临床语言模型的概念验证的开发和控制测试

Cem Simsek¹, Mete Ucdal², Enrique de-Madaria³

¹Gastroenterology & Hepatology, Johns Hopkins Medical Institutions Campus, Baltimore, United States.

Endoscopy international open

|August 27, 2025

概括

在胃肠病学任务中,专门的人工智能 (AI) 模型GastroGPT显著优于一般的大型语言模型 (LLM). 这种临床人工智能有望改善目前LLM能力之外的医疗应用.

科学领域:

医学的人工智能
临床自然语言处理
胃肠道学AI应用

背景情况:

通用AI大语言模型 (LLM) 具有有限的临床实用性,主要用于文档和总结.
需要专门的AI模型来提高复杂的医疗领域的性能.
由于其多样化的临床任务,胃肠道学对人工智能提出了独特的挑战.

研究的目的:

开发和评估一个新的,专业的,多任务的临床胃肠病学LLM.
将GastroGPT与主要的通用LLM (GPT-4,Bard,Claude) 的性能进行比较.
在各种胃肠病例和临床任务中评估人工智能模型的有效性.

主要方法:

一个结构化的比较GastroGPT与三个最先进的通用LLMs.
在七个关键胃肠病学任务和10个不同复杂度的模拟病例中进行评估.
专家小组使用10分利克尔特尺度和统计分析对临床效用进行了盲目评估.

主要成果:

与GPT-4 (5. 2),Bard (5. 7) 和Claude (7. 0) 相比,GastroGPT的整体得分明显更高 (P< 0. 001).
在七项临床任务中的六项中,GastroGPT的表现优于一般的LLM,并且显示出优异的得分一致性.
与一般模型不同的是,GastroGPT的病例复杂性一致 (P < 0. 001).

结论:

与胃肠病学中的通用LLM相比,GastroGPT具有更高的临床效用和任务性能.
针对特定专业的人工智能模型比针对医疗应用的一般模型具有显著的优势.
这项研究突显了定制人工智能解决方案的潜力,

关键词:

内镜小肠内镜上部肠道炎症性肠道疾病新生病没有的出血胰腺胆疾病 (ERCP/PTCD)流动性疾病

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