用AI代理来增强慢性胃炎管理的大型语言模型:全面的比较研究
1Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave, Wuhan, Hubei, 430000, China, +86 188-0276-3109.
JMIR medical informatics
|November 14, 2025
概括
人工智能 (AI) 代理商通过优于大型语言模型 (LLM) 显著改善慢性胃炎管理. 这些人工智能代理为患者提供可靠,可解释和实际的支持,增强自我管理,并可能减少医生工作量.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 慢性胃炎的患病率很高,如果没有及时干预,可能会发展为胃癌.
- 有效的管理需要全面改变生活方式和患者的自我管理,因为有限的持续专业后续.
- 大型语言模型 (LLM) 对患者管理有希望,但存在诸如幻觉和范围限制等局限性.
研究的目的:
- 探索人工智能 (AI) 药物的有效性,以支持慢性胃炎患者的诊断后自我管理.
- 从多个角度评估人工智能代理人的表现,包括安全性,稳定性和与现有LLMs的比较.
主要方法:
- 开发了一个AI代理框架,将LLMs与检索增强生成和搜索引擎工具集成在一起.
- 收集了来自临床环境的真实患者问题,以测试框架在各种难度级别和场景中的性能.
- 通过重复实验分析安全性和稳定性,并使用自动化指标和专家手动评估与最先进的LLM进行性能比较.
主要成果:
- 在处理高度复杂的问题 (82.849比77.825平均分) 和临床咨询任务方面,人工智能代理显著超过了LLM.
- 医生评价表明高安全性 (4.98/5) 与最小的响应变化 (嵌入平均分数的平均绝对偏差为0.0167).
- 与慢性胃炎管理中的先进医学和一般领域的LLM相比,AI药物表现出更高的可靠性,可解释性和实际适用性.
结论:
- 基于LLM的AI剂在慢性胃炎管理中具有显著的应用价值.
- 这些药物可以有效地指导患有慢性疾病的患者,潜在地减少医生的工作量并提高家庭护理质量.
- 这些发现支持在医疗保健环境中更广泛地采用人工智能剂来治疗慢性疾病.
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