代理人工智能和放射学中的大型语言模型:机会和幻觉挑战
Sara Salehi1, Yashbir Singh2, Kelly K Horst1
1Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Bioengineering (Basel, Switzerland)
|December 30, 2025
概括
代理的人工智能 (AI) 通过使用多代理系统和检索增强生成 (RAG) 在放射学中减少幻觉方面表现有前途. 为了获得临床信任和安全实施,需要进一步验证.
科学领域:
- 医疗成像中的人工智能
- 放射学工作流的优化 工作流的优化
- 在医疗保健中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在放射学中越来越多地使用.
- 幻觉 (产生不正确的信息) 是该领域LLM的主要信任障碍.
研究的目的:
- 审查代理人工智能 (AI) 方法来减少放射学中的LLM幻觉.
- 评估多代理系统的潜力,检索增强生成 (RAG) 和不确定性量化.
主要方法:
- 全面审查新兴的代理人工智能策略.
- 评估多因素框架,RAG和不确定性量化.
- 从2024-2025年对诊断准确性和错误减少的证据分析.
主要成果:
- 代理人工智能可以提高诊断准确度,减少放射学中的错误.
- 多代理系统提供交叉验证和工作流程编排.
- 通过在医学文献中对答案进行接地,RAG提高了准确性.
- 不确定性量化有助于在多代理系统中的协作分析.
结论:
- 代理人工智能,特别是多代理框架和RAG,在减轻放射学LLM幻觉方面显示出显著的希望.
- 计算需求和缺乏全面的临床验证是当前的局限性.
- 安全的临床部署需要人类监督,量身定制的评估指标和监管适应.
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