Large Language Model–Assisted Radiology Reporting: A Retrospective Cohort Study Using the UTAUT Framework to Analyze Workflow Integration and Efficiency Gains

Background Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-world implementation data are limited. Objective To evaluate LLM-assisted workflow impact on radiologist efficiency using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Design, Setting, and Participants: HIPAA-compliant, IRB-approved retrospective cohort study of a single fellowship-trained abdominal radiologist 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 with disease-specific templates. Main Outcome Measures: Inter-study interval time (proxy for interpretation time) compared using Wilcoxon rank-sum tests with Bonferroni correction (α = 0.0125). UTAUT constructs assessed: performance expectancy (efficiency), effort expectancy (training burden), facilitating conditions (infrastructure), and behavioral intention (satisfaction). Results We analyzed 609 studies (495 CT, 114 MRI). LLM assistance significantly reduced inter-study intervals for outpatient CT with contrast (23.0 vs 13.0 minutes; difference 10 minutes; p = 0.0021) and without contrast (18.5 vs 7.0 minutes; difference 11.5 minutes; p = 0.0017). No improvement occurred for MRI with contrast (14.0 vs 16.0 minutes; p = 0.2808) or without contrast (14.0 vs 7.0 minutes; p = 0.0889). The radiologist reported improved work-life balance for CT but neutral satisfaction for complex MRI templates. Training required 10 hours over 5 days. Conclusions LLM-assisted workflow reduced interpretation times for standardized CT studies but not heterogeneous MRI examinations, supporting UTAUT's emphasis on performance expectancy and task–technology fit as adoption drivers. Efficiency gains may reduce documentation burden when tools align with task complexity.