评估大型语言模型作为补充的患者信息资源,用于系统性红斑狼的抗疟疾使用
Pamela Munguía-Realpozo1,2, Claudia Mendoza-Pinto1,2, Ivet Etchegaray-Morales2
1Systemic Rheumatic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Social Security Institute, Puebla, Mexico.
Lupus
|February 27, 2025
概括
大型语言模型 (LLM) 显示出对系统性红斑狼 (SLE) 疟疾药物患者教育的希望. GPT-4表现出卓越的完整性和准确性,突出了改善患者理解的潜力.
科学领域:
- 风湿病学和人工智能的人工智能
- 医疗信息学 医疗信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 系统性红斑狼 (SLE) 患者通常需要抗疟疾治疗.
- 准确和完整的患者信息对于管理SLE和抗疟疾药物的使用至关重要.
- 大型语言模型 (LLM) 是新兴的工具,具有传播健康信息的潜力.
研究的目的:
- 评估LLM (Copilot,GPT-3.5,GPT-4) 关于在SLE中使用抗疟疾药物的准确性,完整性和可重复性.
- 为了比较不同LLM在回答有关SLE抗疟疾药物的以患者为中心的问题方面的表现.
主要方法:
- 关于在SLE中使用抗疟疾药物的13个问题来自患者调查和NHS调查.
- 两位风湿病学家独立评估了Copilot,GPT-3.5和GPT-4的反应.
- 预定义的利克特尺度被用来评价准确性,完整性和可重复性.
主要成果:
- 所有评估的LLM在他们的答案中都表现出高准确度.
- GPT-4获得了最高的完整度得分 (92.3%),明显超过了Copilot (38.5%) 和GPT-3.5 (55.9%).
- 所有LLM的可复制率都很高,从84.6%到92.3%不等.
结论:
- 先进的LLM,特别是GPT-4,显示出显著的潜力,以改善患者对SLE抗疟疗法的理解.
- 法律法规可以帮助弥合患者的信息差距,但需要进一步的研究来优化其临床应用.
- 这些人工智能工具的性能和局限性需要持续调查,以便安全有效地整合到医疗保健环境中.
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