Breitwieser Martin, Zirknitzer Stephan, Poslusny Karolina, Freude Thomas, Scholsching Julia, Bodenschatz Karl, Wagner Anton, Hergan Klaus, Schaffert Matthias, Metzger Roman, Marko Patrick
Department for Orthopedic Surgery and Traumatology, Paracelsus Medical University, 5020 Salzburg, Austria.
Department for Pediatric Surgery, Paracelsus Medical University, 90471 Nürnberg, Germany.
Diagnostics (Basel). 2025 Aug 21;15(16):2117. doi: 10.3390/diagnostics15162117.
: Artificial intelligence (AI) tools for fracture detection in radiographs are increasingly approved for clinical use but remain underutilized. Understanding physician attitudes before implementation is essential for successful integration into emergency care workflows. This study investigates the acceptance of an AI-based fracture detection tool among physicians in emergency care settings, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. : A cross-sectional, pre-implementation survey was conducted among 92 physicians across three hospitals participating in the SMART Fracture Trial (ClinicalTrials.gov: NCT06754137). The questionnaire assessed the four core UTAUT constructs-performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC)-and additional constructs such as attitude toward technology (AT), diagnostic confidence (DC), and workflow efficiency (WE). Responses were collected on a five-point Likert scale. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were performed to assess predictors of behavioral intention (BI). : PE was the strongest predictor of BI (β = 0.5882, < 0.001), followed by SI (β = 0.391, < 0.001), FC (β = 0.263, < 0.001), and EE (β = 0.202, = 0.001). These constructs explained a substantial proportion of variance in BI. WE received the lowest ratings, while internal consistency for SI and BI was weak. Moderator analyses showed prior AI experience improved EE, whereas more experienced physicians were more skeptical regarding WE and DC. However, none of the moderators significantly influenced BI. : Physicians' intention to use AI fracture detection is primarily influenced by perceived usefulness and ease of use. Implementation strategies should focus on intuitive design, targeted training, and clear communication of clinical benefits. Further research should evaluate post-implementation usage and user satisfaction.
用于X光片骨折检测的人工智能（AI）工具越来越多地被批准用于临床，但仍未得到充分利用。在实施之前了解医生的态度对于成功融入急诊护理工作流程至关重要。本研究使用技术接受与使用统一理论（UTAUT）模型，调查了急诊护理环境中医生对基于AI的骨折检测工具的接受情况。
对参与SMART骨折试验（ClinicalTrials.gov：NCT06754137）的三家医院的92名医生进行了一项实施前的横断面调查。问卷评估了UTAUT的四个核心构念——绩效期望（PE）、努力期望（EE）、社会影响（SI）、促进条件（FC）——以及其他构念，如对技术的态度（AT）、诊断信心（DC）和工作流程效率（WE）。回答采用五点李克特量表收集。进行了结构方程建模（SEM）和验证性因子分析（CFA）以评估行为意向（BI）的预测因素。
PE是BI的最强预测因素（β = 0.5882，P < 0.001），其次是SI（β = 0.391，P < 0.001）、FC（β = 0.263，P < 0.001）和EE（β = 0.202，P = 0.001）。这些构念解释了BI中很大一部分方差。WE的评分最低，而SI和BI的内部一致性较弱。调节分析表明，先前的AI经验改善了EE，而经验更丰富的医生对WE和DC更为怀疑。然而，没有一个调节因素对BI有显著影响。
医生使用AI骨折检测的意向主要受感知有用性和易用性的影响。实施策略应侧重于直观的设计、有针对性的培训以及临床益处的清晰传达。进一步的研究应评估实施后的使用情况和用户满意度。
