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在儿科手术中设计以患者为中心的沟通辅助工具,使用大型语言模型

Arya S Rao¹, Aneesh Mazumder², Elizabeth Roux¹

¹Harvard Medical School, Boston, MA, United States; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center, Mass General Brigham, Boston, MA, United States.

Journal of pediatric surgery

|September 10, 2025

概括

大型语言模型 (LLM) 在向儿童解释儿科手术方面表现有前途. 在准确性和适合年龄方面,GPT-4-turbo通常优于Gemini 1.0 Pro,尽管这两种模型都需要进一步改进,以实现公平的患者沟通.

科学领域:

人工智能在医学中的应用
医疗保健通信的自然语言处理.
儿童外科手术患者教育

背景情况:

向不同年龄段的患者传达复杂的儿科外科信息存在重大挑战.
大型语言模型 (LLM) 为将专业医疗信息翻译成易于理解的语言提供了一个潜在的解决方案.
本研究研究了LLM在为常见的儿科手术提供适合年龄的解释方面的有效性.

研究的目的:

评估两个领先的LLM (GPT-4-turbo和Gemini 1.0 Pro) 在产生儿科手术解释方面的表现.
评估LLM产生的内容的准确性,完整性,适龄性和潜在的人口偏差.
确定LLM作为儿科手术中以患者为中心的沟通辅助工具的整体质量和实用性.

主要方法:

两位全科医生法学士被要求向不同年龄和性别的儿童解释四种常见的儿科手术.
儿科医生和整体外科医生用五分利克尔特尺度对答案进行了准确性,完整性,年龄适当性和人口统计偏差等评分.
统计分析,包括顺序混合效应建模,用于评估收集的评级.

主要成果:

两种LLM都产生了总体质量适度高的解释,但GPT-4-turbo在所有措施中总体上获得了更高的评分.
GPT-4-turbo反应被评为高度准确,完整和适合年龄,性能随患者年龄而改善.
双子 1.0 Pro的反应是适度准确和适合年龄的,随着患者年龄的增加,性能往往会下降;有轻微的基于性别的差异.

结论:

现成的LLM证明了产生准确,完整和适合年龄的儿科手术解释的潜力,其偏差很低.
存在显著的模型间变异性,突出了进一步验证和临床内容微调的需要.
法律医疗法可以成为增强患者教育和确保医疗信息在护理地点公平沟通的有价值的工具.

关键词:

适合年龄的解释.人工智能的人工智能是人工智能.数字健康创新是数字健康的创新.大型语言模型 (LLM)以患者为中心的沟通.儿科传播儿科传播儿科外科手术儿科外科手术

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