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关于人工智能生成的核医学成像内容中的幻觉 (DREAM报告)
Menghua Xia1,2, Reimund Bayerlein3,4, Yanis Chemli1,2
1Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut.
概括
人工智能生成的内容 (AIGC) 在核医学成像 (NMI) 中表现有前途,但有风险"幻觉". 该DREAM报告提供了定义,检测和减轻这些AI错误的策略,以实现更安全的临床使用.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 核医学是一种核医学.
背景情况:
- 人工智能生成的内容 (AIGC) 为核医学成像 (NMI) 任务,如图像增强和运动校正提供了成本效益高的解决方案.
- 在NMI中AIGC的一个重大挑战是"幻觉"的风险 - - 现实但事实上不正确的输出.
- 这些人工智能诱导的不准确性可能会扭曲解剖和功能信息,可能会影响诊断的准确性和侵蚀临床信任.
研究的目的:
- 在AIGC中为NMI提供关于幻觉相关问题的挑战的全面视角.
- 介绍DREAM报告,概述了解决NMI中AIGC幻觉问题的建议.
- 促进共同理解和指导未来的研究,以确保AIGC在NMI中安全有效的临床部署.
主要方法:
- 审查和综合目前的挑战和潜在的解决方案,与AIGC在NMI.
- 开发DREAM报告框架,包括定义,示例,检测,评估,归因和缓解策略.
- 基于专家共识和文献审查的立场陈述的制定.
主要成果:
- 确定幻觉风险是AIGC在NMI采用的关键障碍.
- 该DREAM报告提供了一个结构化的方法来理解和管理AIGC幻觉.
- 建议包括定义幻觉,提供代表性例子,建立检测和评估指标,并概述归因和缓解策略.
结论:
- 解决AIGC幻觉对于确保核医学成像中AI应用的可靠性和安全性至关重要.
- 梦想报告是标准化术语和实践的基础文件.
- 需要进一步的研究和合作努力,以提高AIGC在临床NMI环境中的可信度.
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