在临床医学中对大型语言模型 (LLM) 评估进行系统审查
Sina Shool1, Sara Adimi2, Reza Saboori Amleshi1
1Center for Technology and Innovation in Cardiovascular Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
BMC medical informatics and decision making
|March 7, 2025
概括
大型语言模型 (LLM) 在医疗保健方面表现有前途,但需要严格的评估. 本综述强调了临床医学当前的LLM应用,局限性和趋势,强调了安全整合的标准化框架.
科学领域:
- 人工智能在医学中的应用
- 在医疗保健中的自然语言处理.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 在临床医学中具有显著的潜力,包括增强诊断,决策支持和医学教育.
- 将LLM整合到临床工作流中需要对可靠性,安全性和伦理方面的考虑进行彻底评估.
研究的目的:
- 系统地审查临床医学LLM的评估参数和方法.
- 突出医学领域的LLM能力,局限性和应用趋势.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,Web of Science,IEEE Xplore,arXiv) 中进行全面的文献搜索.
- 包括同行评审和预印研究,评估在医疗环境中LLM的表现.
- 基于预定义的纳入和排除标准对研究进行选.
主要成果:
- 761项研究符合纳入标准,表明对临床环境中的LLM日益感兴趣.
- 一般领域的LLM (例如,ChatGPT,GPT-4) 在评估中占主导地位 (93.55%),而医学领域的LLM代表性不足 (6.45%).
- 准确度是最常被评估的参数 (21.78%),尽管在各个研究中注意到了局限性和偏见.
结论:
- 法学士研究的快速增长意味着医疗保健的变革潜力.
- 解决伦理风险,评估变化和专业代表性不足对于LLM采用至关重要.
- 标准化框架对于安全,有效和公平地将LLMs纳入临床实践至关重要.
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