基于人工智能的患者教育模型对刺激性肠综合征的评估
Anand Kumar Raghavendran1, Balaji Musunuri1, Siddheesh Rajpurohit1
1Department of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
像双子座和ChatGPT这样的大型语言模型 (LLM) 为刺激性肠综合征 (IBS) 患者提供了广泛的信息,但可读性和同理心需要改进,以改善患者教育.
科学领域:
- 医疗保健中的人工智能
- 医疗信息的自然语言处理.
- 患者教育 技术 技术
背景情况:
- 刺激性肠综合征 (IBS) 是一种普遍存在的功能性胃肠道疾病,具有显著的心理社会影响.
- 现有的IBS患者教育往往不足,强调需要改进资源.
- 大型语言模型 (LLM) 是一种潜在的工具,可以提高患者对IBS的理解.
研究的目的:
- 为了比较两个领先的LLM,ChatGPT-4和Gemini-1在解决与IBS相关的患者查询方面的表现.
- 评估LLM生成的对IBS信息的响应的准确性,全面性,可读性和同理心.
- 为了确定基于LLM的功能性胃肠道疾病患者教育的改进领域.
主要方法:
- 关于IBS的39个常见问题 (FAQ) 来自知名的患者组织和医院网站.
- 问题被分为六个领域:一般理解,症状/诊断,原因,饮食,治疗和生活方式.
- 来自ChatGPT-4和Gemini-1的响应被胃肠病学家对准确性和全面性进行了评估,并获得了可读性和同理心分数.
主要成果:
- 与ChatGPT (89.7%) 相比,Gemini表现出更高的准确性和全面性 (94.9%),尽管差异在统计学上并不显著.
- 这两种模型在"症状和诊断"和"治疗"领域表现最好,在"一般理解"和"生活方式"信息方面存在挑战.
- 可读性分析表明,两种LLM的内容都难以理解,而ChatGPT在其响应中表现出更高的同情度.
结论:
- 虽然像ChatGPT和Gemini这样的LLM可以提供大量IBS信息,但它们目前的局限性包括复杂的语言和偶尔的不准确性.
- 未来的LLM代应该优先考虑增强可读性,上下文相关性和改进的准确性,以有效地为患者和临床医生提供服务.
- 需要进一步的研究来优化LLM产生的内容,以便在胃肠病学中为患者提供可访问和富有同情心的教育.
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