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分析大型语言模型对常见的腰椎脊柱融合手术问题的反应:ChatGPT和Bard之间的比较

Siegmund Philipp Lang^1,2, Ezra Tilahun Yoseph¹, Aneysis D Gonzalez-Suarez¹

¹Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.

|July 2, 2024

概括

像ChatGPT和Bard这样的大型语言模型有效地回答患者关于腰椎融合手术的问题. 然而,需要进一步的研究来优化它们在患者教育和医疗保健沟通中的作用.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
脊椎外科患者教育患者教育

背景情况:

患者越来越多地在网上寻找健康信息,包括腰椎融合的细节.
大型语言模型 (LLM) 为患者教育提供了潜力,但需要进行评估.

研究的目的:

评估通过ChatGPT 3.5和谷歌Bard提供关于腰椎融合手术的患者查询的响应质量.
为了比较两个领先的LLM在提供关于脊髓融合程序的准确和可理解信息方面的表现.

主要方法:

从更大的常见问题集中选择了关于腰椎融合的十个关键问题.
来自ChatGPT 3.5和Bard的响应被五位失明的脊椎外科医生使用4分评级表来评估.
回答的清晰度和专业性使用5分利克特级别进行评估.

主要成果:

几乎所有的答案 (97%) 从两个LLMs被评为优秀或令人满意.
聊天GPT 3.5和Bard的表现相似,分别有62%和66%的优秀评分.
两种模型都在解决有关手术风险,成功率和方法选择的具体问题上表现出局限性,观察到的可靠性较低.

结论:

聊天GPT 3.5和巴德可以有效地回答关于腰椎融合的常见问题.
进一步开发和验证是必要的,以充分整合LLMs在医学教育和患者沟通策略.
虽然LLM很有前途,但需要改进,以确保一致的准确性,并全面解决复杂的外科查询.

关键词:

人工智能的人工智能是人工智能.巴尔德巴尔德巴尔德巴尔德是什么意思聊天GPT 聊天在GPT 聊天大型语言模型.腰椎脊柱融合腰椎脊柱融合患者教育患者教育

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