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评估大型语言模型的研究环境和临床实用性:一个范围审查

Ye-Jean Park¹, Abhinav Pillai², Jiawen Deng³

¹Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, M5S 1A8, Toronto, ON, Canada. yejean.park@mail.utoronto.ca.

BMC medical informatics and decision making

|March 13, 2024

概括

大型语言模型 (LLM) 在医疗保健中对笔记编译和患者导航等任务具有前景. 然而,由于道德问题,数据偏差和准确性问题,安全的临床实施需要标准化的评估框架.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床研究临床研究

背景情况:

大型语言模型 (LLM) 在合成医疗应用的自然语言方面显示出巨大潜力.
现有研究强调了临床环境中LLMs的功能和局限性.
在医学LLM的评估,应用和证据基础上仍然存在差距.

研究的目的:

审查目前关于LLM在医疗应用中的准确性和有效性的证据.
讨论LLM在医疗保健中的道德,法律,后勤和社会经济影响.
提出一个标准化的框架来评估LLM的临床效用,并确定未来的研究方向.

主要方法:

从主要数据库 (MEDLINE,EMBASE等) 中对4036个记录进行了全面的范围审查. 和预印服务器.
分析了2023年1月至2023年6月以英语出版的55项全球研究.
使用牛津证据医学中心建议评估证据质量.

主要成果:

在患者笔记编制,医疗护理导航援助以及在人类监督下支持临床决策方面,LLM是有前途的.
关键的局限性包括数据偏差,生成不准确的信息,以及道德,法律,社会经济和隐私方面的担忧.
发现了评估LLM有效性和可行性的标准化方法的严重缺乏.

结论:

在医疗保健方面,LLM提供了潜在的好处,但需要仔细考虑其局限性.
解决偏见,准确性和道德问题对于安全实施至关重要.
需要进一步的研究和标准化的评估,才能充分发挥LLMs在临床应用中的潜力.

关键词:

聊天GPT 聊天在GPT 聊天临床环境临床环境大型语言模型.自然语言处理自然语言处理.范围审查范围审查

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