ArticleAbdominal radiology (New York)2026
Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper 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
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1 author.
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Abstract
backgroundRadiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-world implementation data are limited.
objectiveTo evaluate the impact of an LLM-assisted workflow on radiologist efficiency using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. DESIGN, SETTING, AND
participantsHIPAA-compliant, IRB-approved retrospective cohort study of a single fellowship-trained abdominal radiologist exploratory study at Mayo Clinic Arizona. We compared baseline (January-April 2024) and post-implementation (December 2025-February 2026) periods. A custom generative pre-trained transformer was developed using ChatGPT Enterprise Model 5.2 Thinking with disease-specific templates.
main outcome measuresInter-study interval time, used as a proxy for interpretation time, compared using Wilcoxon rank-sum tests with Bonferroni correction (α = 0.01). UTAUT constructs assessed: performance expectancy (efficiency), effort expectancy (training burden), facilitating conditions (infrastructure), and behavioral intention (satisfaction).
resultsWe analyzed 609 studies (495 CT, 114 MRI). LLM assistance significantly reduced inter-study intervals for outpatient CT with contrast (23.0 vs. 13.0 min; difference 10 min; p = 0.0021) and without contrast (18.5 vs. 7.0 min; difference 11.5 min; p = 0.0017). No improvement occurred for MRI with contrast (14.0 vs. 16.0 min; p = 0.2808) or without contrast (14.0 vs. 7.0 min; p = 0.0889). The radiologist reported improved work-life balance for CT but neutral satisfaction for complex MRI templates. Training required 10 h over 5 days.
conclusionsLLM-assisted workflow reduced inter-study interpretation times for standardized CT studies and no clear efficiency benefit was observed for MRI in this small implementation sample, when interpreted through a UTAUT lens, particularly on performance expectancy and task-technology fit as adoption drivers. Efficiency gains may reduce documentation burden when tools align with task complexity.
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