ArticleEJNMMI research2025
Automation of
Article in EJNMMI research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Consensus-Level and Cluster-Adjusted Evaluation of a Large Language Model for Diagnostic Extraction from Musculoskeletal Radiology Reports.Diagnostics (Basel, Switzerland) · 2026Article
- Conceptual proposal for LLM-generated FDG PET/CT follow-up reports in melanoma: a pilot study on model stability and blinded expert evaluation.Frontiers in nuclear medicine · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background99mTc mercaptoacetyltriglycine (MAG3) studies represent a classic of nuclear medicine. We evaluated the quality of MAG3 reports generated by a vision language model (VLM). The mid-term goal to utilize such systems could be the support of nuclear medicine experts, thereby freeing staff resources currently tied up in administrative and documentation tasks.
resultsHuman raters generally preferred MAG3 reports written by human authors over those generated using the VLM. Scores for VLM-generated reports improved notably in the subset of cases where the urodynamic curves were correctly identified by the model. Half of the raters found statistically significant differences between human and Qwen-generated reports (Wilcoxon signed-rank test, alpha = 0.05). There was a strong correlation between the assessed text quality and the raters’ ability to correctly identify the source of the text (human or Qwen; R2 = 0.998).
conclusionThe VLM-based approach successfully generated MAG3 reports that, to some extent, resembled those written by human experts. However, assessed quality varied among raters. It remains to be seen whether future language model advancements will reach a performance level sufficient for integration into clinical practice.
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.