On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918195247448064 |
|---|---|
| author | Xia, Menghua Bayerlein, Reimund Chemli, Yanis Liu, Xiaofeng Ouyang, Jinsong Lin, MingDe Fakhri, Georges El Badawi, Ramsey D. Li, Quanzheng Liu, Chi |
| author_facet | Xia, Menghua Bayerlein, Reimund Chemli, Yanis Liu, Xiaofeng Ouyang, Jinsong Lin, MingDe Fakhri, Georges El Badawi, Ramsey D. Li, Quanzheng Liu, Chi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report) Xia, Menghua Bayerlein, Reimund Chemli, Yanis Liu, Xiaofeng Ouyang, Jinsong Lin, MingDe Fakhri, Georges El Badawi, Ramsey D. Li, Quanzheng Liu, Chi Image and Video Processing 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. |
| title | On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report) |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2506.13995 |