ReviewJournal of nuclear medicine : official publication, Society of Nuclear Medicine2026
On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).
Review in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.Global epidemiology · 2026Article
- Review
- Letter to the Editor on Generative AI-Enhanced Low-Dose CBCT for Impacted Mandibular Third Molars.International dental journal · 2026Article
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- Large Language Model-Based Interactive Code Generation for Developing a 3D Eye Movement Schematic.Cureus · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.