ArticleFrontiers in nuclear medicine2026
Conceptual proposal for LLM-generated FDG PET/CT follow-up reports in melanoma: a pilot study on model stability and blinded expert evaluation.
Article in Frontiers in nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Consensus-Level and Cluster-Adjusted Evaluation of a Large Language Model for Diagnostic Extraction from Musculoskeletal Radiology Reports.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
14 authors.
Funding
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Abstract
Purpose: Oncological patients regularly undergo PET/CT re-staging, which requires a report that outlines their current disease status and highlights relevant changes compared to the previous PET/CT. Large language models (LLMs) may be helpful with documentation in the future. This study is a pilot on LLM performance, focusing on test-retest stability and reproducibility. Methods: Three textbook melanoma follow-up cases of increasing complexity (involving one to eight organs) were selected. From standardized text-only prompts (no imaging data), follow-up reports were written by GPT-4o, Claude Sonnet 4 (each producing three independent revisions), and three nuclear medicine residents. This yielded nine reports per case (27 in total). Six blinded nuclear medicine experts (three internal, three external) performed test-retest evaluations of report quality and authorship identification. Results: The cosine similarity analysis revealed high intra-case coherence (mean: 0.599-0.727) regardless of authorship. The external human readers consistently rated reports higher than the internal human readers. The LLM-generated reports received comparable or superior ratings to human reports, with Claude achieving the highest external reader scores (mean 0.926, standard deviation 0.263, on a 0-1 scale). Human performance declined with case complexity, while Claude, in particular, improved. The external readers significantly preferred the LLM impressions (Fisher's exact test, Conclusion: In this pilot, blinded expert evaluation demonstrated that current LLMs can generate reports for melanoma [
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