系统性审查:使用大型语言模型作为医疗聊天机器人在消化系统疾病中的使用
Mauro Giuffrè1,2, Simone Kresevic3, Kisung You4
1Department of Internal Medicine (Digestive Diseases), Yale School of Medicine, New Haven, Connecticut, USA.
Alimentary pharmacology & therapeutics
|May 27, 2024
概括
大型语言模型 (LLM) 显示胃肠病学和肝病学问题的准确性较低,造成患者安全风险. 需要进一步的研究来提高临床环境中的LLM可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 胃肠病学和肝病学研究研究
背景情况:
- 越来越多地使用大型语言模型 (LLM),如ChatGPT,用于医疗咨询和临床决策支持.
- 关于胃肠病学和肝病学LLM反应准确性的不确定性.
研究的目的:
- 评估LLM用于诊断,管理和治疗胃肠病和肝病的准确性.
- 评估与LLM在这些专业中的使用相关的潜在患者安全影响.
主要方法:
- 在主要数据库 (Cochrane,PubMed,Scopus等) 进行系统的文献搜索. 在2024年1月28日之前.
- 包括来自1671份选报告的18篇全文文章.
- 准确度定义为完全正确答案的百分比.
主要成果:
- 根据模型版本,LLM的准确性差异很大,其中ChatGPT-3.5 (6.4%-45.5%) 和ChatGPT-4 (40%-91.4%) 存在显著差异.
- 研究显示,由于缺乏标准化的方法和报告指标,存在偏见的高风险.
- 由于研究设计的局限性,结果无法概括.
结论:
- 目前的通用LLM在临床胃肠病学和肝病学任务中表现出不可接受的低准确性.
- 不准确的LLM输出可能会导致不利的患者安全事件,可能导致系统过度负担或延迟护理.
- 在临床整合之前需要提高LLM准确性和标准化评估.
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