Background/Objectives: 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. Methods: 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). Results: PE was the strongest predictor of BI (β = 0.5882, p < 0.001), followed by SI (β = 0.391, p < 0.001), FC (β = 0.263, p < 0.001), and EE (β = 0.202, p = 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. Conclusions: 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.

Infections after joint arthroplasties represent a devastating and progressively escalating complication with increased morbidity and mortality. The eradication of biofilms from infected implants is still an unsolved challenge. The erbium‐doped yttrium aluminum garnet (Er:YAG) laser, which delivers high‐energy light for rapid tissue ablation, may offer an advancement. This study aimed to evaluate the effectiveness of this laser in removing biofilms from infected implant surfaces. In this prospective study, 31 patients with 33 early postoperative or acute hematogenous periprosthetic joint infections (PJIs) were treated with our modified procedure of debridement, antibiotics, laser irradiation and implant retention (DALIR). Biofilm removal was compared between mechanical cleansing alone and the additional use of Er:YAG laser light. Therefore, swab cultures from the implants were taken at three distinct occasions: post‐arthrotomy, after mechanical cleansing with a fluid disinfectant (LavaSurge), and after additional Er:YAG laser irradiation. The success rate of the DALIR procedure was compared with a prior group ( n = 34) that underwent DAIR procedures without the Er:YAG laser at our clinic. The implementation of the laser system in our DAIR procedure was uncomplicated. The additional Er:YAG laser therapy significantly reduced viable microorganisms on implant surfaces (9.1%) compared to mechanical cleaning alone (42.4%; p < 0.01). The healing rate in our cohort was 78.1%, a substantial improvement over the previous rate of 44.1% ( p < 0.01). Therefore, we recommend the use of Er:YAG laser irradiation as an additional tool for surface disinfection of metal implants in PJIs whenever a DAIR procedure seems to be beneficial. Trial Registration: ClinicalTrials.gov identifier: NCT06440564. Level of Evidence: 2B.