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使用人工智能为泌尿病患者生成医学文献:比较三种不同的大型语言模型

David Pompili¹, Yasmina Richa¹, Patrick Collins²

¹School of Medicine, University College Cork, Cork, Ireland.

World journal of urology

|July 29, 2024

概括

大型语言模型 (LLM) 显示出产生泌尿病学主题患者信息传单的潜力. 然而,内容需要临床审查以确保准确性和对成年患者的可读性.

科学领域:

医疗保健中的人工智能
医疗信息学医疗信息学
自然语言处理自然语言处理.

背景情况:

大型语言模型 (LLM) 是先进的AI系统,能够生成类似人类的文本.
在医疗保健中应用LLM对改善患者教育和减少临床医生的工作负担具有显著的前景.
患者信息传单 (PIL) 对于患者了解疾病和治疗方法至关重要.

研究的目的:

评估三个领先的LLM在常见泌尿病状况上生成的患者信息传单 (PIL) 的质量.
为了比较ChatGPT-4,PaLM 2 (Google Bard) 和Llama 2 (Meta) 在创建泌尿学专注的PILs方面的性能.
评估LLM生成的PIL的可读性和准确性,以帮助患者理解.

主要方法:

三名LLM (ChatGPT-4,PaLM 2,Llama 2) 被要求为四个泌尿学主题生成PIL:割礼,切除术,过活性膀综合征和前列腺的穿尿管切除.
用标准化质量检查清单来评估生成的PIL.
使用平均阅读水平共识计算器来评估可读性.

主要成果:

PaLM 2平均产生了最高质量的PIL (3.58),其次是Llama 2 (3.34) 和ChatGPT-4 (3.08).
PaLM 2 是唯一包含图像的模型,并且在大多数主题中都表现出色,除了 TURP.
所有LLM生成的PIL都含有医学不准确性,可读性通常高于平均成年阅读水平 (PaLM 2: 14-15岁,Llama 2: 16-17岁).

结论:

LLM可以帮助产生PIL,从而减轻医疗保健专业人员的负担.
临床医生监督至关重要,以确保准确性,并纳入像图像这样的健康素养元素.
需要对LLM算法和快速工程进行改进,以提高成人患者的PIL可读性.

关键词:

人工智能 (AI) 是一种人工智能.聊天GPT 聊天GPT 聊天在谷歌上搜索Bard.大型语言模型 (LLM)患者教育患者教育患者信息传单患者信息传单

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