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潜在的错误信息在大语言模型描述上肢疾病的描述

George E Sayegh¹, David Ring¹, Prakash Jayakumar¹

¹Department of Surgery and Perioperative Care Dell Medical School, Austin, TX, USA.

The Journal of hand surgery, European volume

|August 22, 2024

概括

讨论上肢状况的大型语言模型经常存在错误信息. 这可以加强无助的思维,并减少患者的健康机构.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
患者教育患者教育

背景情况:

在线健康信息的大量传播需要准确可靠的来源.
大型语言模型 (LLM) 越来越多地用于获取健康信息,引发了对潜在错误信息的担忧.
上肢疾病代表了患者调查和自我管理的共同领域.

研究的目的:

评估关于上肢条件的大型语言模型 (LLM) 响应的准确性和影响.
评估LLM是否能够在被要求时有效纠正错误信息.
调查LLM产生的内容对患者健康机构和决策的影响.

主要方法:

一个大型语言模型被提示对常见的上肢状况进行查询.
随后,LLM被要求在自己生成的响应中识别和纠正错误信息.
分析的重点是错误信息的持续性,用户思维模式的变化,以及报告对自我管理的影响.

主要成果:

尽管提醒纠正错误信息,LLM在讨论上肢状况时表现出持续的不准确性.
士学位的回应倾向于强化无助的与健康相关的思维模式.
在与LLM互动的用户中,独立健康管理 (机构) 的意义显著减少.

结论:

目前的大型语言模型在提供关于上肢状况的准确和公正信息方面存在局限性.
法律医疗管理师倾向于延续错误信息并减少患者的代理权,这对有效的健康管理构成风险.
在LLM可靠地用于医疗条件的患者教育之前,需要进一步开发和严格验证.

关键词:

聊天GPT 聊天GPT 聊天大型语言模型.错误的信息错误的信息上肢疾病上肢疾病

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