DeepGut:一种协作式多式大型语言模型框架,用于消化系统疾病的辅助诊断和治疗
Xiao-Han Wan1, Mei-Xia Liu1, Yan Zhang1
1Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, China.
World journal of gastroenterology
|September 3, 2025
概括
一个新的多式大型语言模型 (LLM) 框架DeepGut通过整合多种患者数据来提高胃肠道疾病的诊断准确性. 这种人工智能工具提供了改进的诊断和治疗建议,超越单一模式系统.
科学领域:
- 医学的人工智能
- 消化系统疾病的诊断
- 临床决策支持系统
背景情况:
- 胃肠道疾病存在复杂的诊断挑战,需要整合各种患者数据.
- 目前的AI诊断工具通常依赖于单一的数据类型,导致不完整的建议和潜在的风险.
- 医生需要先进的工具来综合病史,实验室结果,
研究的目的:
- 为改善消化系统疾病的临床决策制定和评估一个协作式的多式大型语言模型 (LLM) 框架.
- 创建一个能够整合多种数据源的AI系统,以提供全面的诊断支持.
- 评估一款新型多式LLM在帮助医生进行胃肠道诊断方面的性能和可靠性.
主要方法:
- 开发了DeepGut,这是一个集成四个大型模型的四层多式合作LLM框架.
- 实施顺序处理以获取多模式信息,构建逻辑链并生成诊断/治疗建议.
- 通过客观的可靠性和全面性指标以及主观的专家评估来评估框架.
主要成果:
- DeepGut获得了高性能:97. 8%的诊断准确度,93. 9%的诊断完整性,95. 2%的治疗计划准确度和98. 0%的治疗计划完整性.
- 多模式LLM协作显著超过单模式AI诊断工具.
- 专家评审赞扬人工智能产生的成果的完整性,相关性和逻辑一致性.
结论:
- DeepGut框架成功地整合了多模式诊断数据,以加强人工智能辅助的消化系统疾病临床决策.
- 多模式LLM合作显示出在医疗保健领域推进人工智能应用的巨大潜力.
- 这种方法为利用人工智能在复杂的诊断场景中支持医生开辟了新的途径.
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