On Hallucinations in Artificial IntelligenceGenerated Content for Nuclear Medicine Imaging (the DREAM Report)

作者：Menghua Xia, Reimund Bayerlein, Yanis Chemli, Xiaofeng Liu, Jinsong Ouyang, MingDe Lin, Georges El Fakhri, Ramsey D. Badawi, Quanzheng Li, Chi Liu · 发表于：Journal of Nuclear Medicine · 年份：2025 · DOI：10.2967/jnumed.125.270653 · 被引用次数：4 · 研究领域：Medical Imaging Techniques and Applications、Adversarial Robustness in Machine Learning、Radiation Detection and Scintillator Technologies

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 anatomic and functional information, compromising diagnostic accuracy and clinical trust. This paper presents a comprehensive perspective on hallucination-related challenges in AIGC for NMI, introducing the DREAM report, which covers recommendations for definition, representative examples, detection and evaluation metrics, and attributions 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.