大语言模型辅助放射学报告：单放射科医师实施的回顾性队列研究及UTAUT框架解读
本研究在单中心单放射科医师场景下评估大语言模型辅助工作流对效率的影响，比较基线期与实施后期间阅片间隔时间。结果显示，LLM辅助显著缩短了增强和非增强门诊CT的阅片间隔，但对MRI未见明确效率提升。基于UTAUT框架，绩效期望和任务-技术匹配是采纳的关键驱动因素。
要点
- LLM辅助可缩短标准化CT的阅片间隔时间，但对MRI效果不明确。
- 使用LLM需约10小时、5天的培训，报告者工作生活平衡在CT方面改善。
- 任务复杂度和技术匹配度影响LLM辅助效率的发挥。
英文原文（摘要）· 点击展开
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 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 PARTICIPANTS: HIPAA-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 MEASURES: Inter-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).
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 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.
CONCLUSIONS: LLM-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.
引用
- Tan N. Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens. Abdom Radiol (NY). 2026. PMID: 42009972. doi:10.1007/s00261-026-05524-y
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。