AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Healthcare
• Health Services Research

Background:

• Artificial intelligence (AI) tools for fracture detection are approved but underutilized in clinical settings.
• Understanding physician attitudes is crucial for successful AI integration into emergency care.
• The Unified Theory of Acceptance and Use of Technology (UTAUT) model provides a framework for assessing AI adoption.

Purpose of the Study:

• To investigate physician acceptance of AI-based fracture detection tools in emergency care.
• To identify key factors influencing physicians' behavioral intention to use AI for fracture detection.
• To assess the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on AI adoption.

Main Methods:

• A cross-sectional survey of 92 physicians across three hospitals was conducted.
• The Unified Theory of Acceptance and Use of Technology (UTAUT) model constructs were assessed using a five-point Likert scale.
• Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were used to analyze predictors of behavioral intention.

Main Results:

• Performance expectancy (perceived usefulness) was the strongest predictor of behavioral intention to use AI for fracture detection.
• Social influence, facilitating conditions, and effort expectancy also significantly predicted behavioral intention.
• Workflow efficiency received the lowest ratings; prior AI experience did not significantly influence behavioral intention.

Conclusions:

• Physician intention to adopt AI fracture detection is driven by perceived usefulness and ease of use.
• Implementation strategies should prioritize intuitive design, targeted training, and clear communication of clinical benefits.
• Further research is needed to evaluate post-implementation usage and user satisfaction with AI tools.