On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)

TL;DR

This paper discusses the challenges of hallucinations in AI-generated nuclear medicine images, emphasizing the need for standardized detection, evaluation, and mitigation strategies to ensure safe clinical use.

Contribution

It introduces the DREAM report, providing a comprehensive overview of hallucination issues and recommendations for improving AI-generated content in nuclear medicine imaging.

Findings

Identifies key causes of hallucinations in AIGC for NMI

Proposes metrics for detecting and evaluating hallucinations

Recommends strategies to mitigate hallucination risks

Abstract

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.