# On Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report)

## TL;DR

This paper discusses the risks of hallucinations in AI-generated nuclear medicine imaging and introduces the DREAM report to address these issues and improve clinical trust.

## Contribution

The paper introduces the DREAM report, a framework for understanding and mitigating hallucinations in AI-generated nuclear medicine imaging.

## Key findings

- Hallucinations in AI-generated nuclear medicine imaging can misrepresent anatomic and functional information.
- The DREAM report provides recommendations for defining, detecting, and mitigating hallucinations in AIGC for NMI.

## 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 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.