揭示AI生成影像中的幻觉问题，推动核医学安全应用
On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)
- 提出DREAM报告框架，系统梳理幻觉定义与案例
- 指出幻觉会扭曲解剖功能信息，影响诊断可信度
- 适合临床医生与AI研发者参考，防范生成内容风险
人工智能生成内容（AIGC）在核医学成像（NMI）中表现优异，可实现图像增强、运动校正和衰减校正等低成本软件解决方案。然而，此类技术存在幻觉风险，可能生成看似真实但事实错误的内容。这些幻觉会歪曲解剖与功能信息，损害诊断准确性并削弱临床信任。本文提出全面的幻觉挑战视角，发布DREAM报告，涵盖幻觉的定义、代表性案例、检测与评估指标、根本原因及缓解策略。本立场论文旨在促进对AIGC在NMI中应用的共识，推动安全有效的临床部署。
原文摘要 · Abstract (English)
Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as image enhancement, motion correction, and attenuation correction. However, these advancements come with the risk of hallucinations, generating realistic yet factually incorrect content. Hallucinations can misrepresent anatomical and functional information, compromising diagnostic accuracy and clinical trust. This paper presents a comprehensive perspective of hallucination-related challenges in AIGC for NMI, introducing the DREAM report, which covers recommendations for definition, representative examples, detection and evaluation metrics, underlying causes, and mitigation strategies. This position statement paper aims to initiate a common understanding for discussions and future research toward enhancing AIGC applications in NMI, thereby supporting their safe and effective deployment in clinical practice.
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