评估大型语言模型在生成2型糖尿病管理计划中的性能和安全性:与使用真实患者记录的医生进行比较研究
Agnibho Mondal1, Arindam Naskar2, Bhaskar Roy Choudhury3
1Department of Infectious Diseases and Advanced Microbiology, School of Tropical Medicine, Kolkata, IND.
Cureus
|April 18, 2025
概括
像GPT-4这样的大型语言模型可以减少2型糖尿病患者不必要的处方,但缺乏医生的完整性. 人工智能显示承诺作为一个补充工具与人类监督安全的临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 在医疗保健领域具有潜力,从科学写作到个性化医疗.
- 临床应用LLMs需要严格评估准确性,道德和偏见.
- 对医学标准进行LLM实用性和安全性的评估至关重要.
研究的目的:
- 将GPT-4生成的临床管理计划与2型糖尿病患者医生的临床管理计划进行比较.
- 评估人工智能生成与医生生成的管理计划的完整性,必要性和剂量准确性.
- 评估GPT-4的临床管理计划的安全性.
主要方法:
- 来自印度西孟加拉邦的匿名患者记录的比较分析.
- GPT-4和三个医生为50名2型糖尿病患者生成了管理计划.
- 计划根据美国糖尿病协会的指导方针进行评估,量化完整性,必要性和剂量准确性.
主要成果:
- 医生计划中缺少的药物较少 (p=0.008),而GPT-4中缺少的药物较少 (p=0.003).
- 在GPT-4和医生之间,药物剂量准确度 (p=0.975) 或整体误差得分 (p=0.301) 没有显著差异.
- 在16%的案例中,GPT-4生成的计划存在安全问题.
结论:
- GPT-4有效地减少了不必要的处方,但在糖尿病管理中不符合医生的完整性.
- 在临床环境中,LLM显示出作为补充工具的潜力.
- 改进的算法和持续的人类监督对于在医疗保健中安全有效地实施人工智能至关重要.
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