Integrating artificial intelligence into surgical nursing workflows: Evaluation of an image-based Instrument verification system

ObjectiveThis study compared an image-based, artificial intelligence-driven instrument recognition and inventory system (AI-IRIS) with conventional manual counting (CMC) for postoperative instrument management in simulated settings.MethodsUsing a randomized crossover design, 34 operating room nurses completed two standardized counting scenarios with both methods and rated adoption using the Diffusion of Innovations Questionnaire (DOIQ).ResultsRecognition accuracy was identical between AI-IRIS and CMC under controlled, simulated conditions. However, the AI-IRIS significantly reduced counting time by 26-28 seconds per scenario, representing an approximately 33-35% reduction compared with manual counting (both p < 0.001), and achieved higher DOIQ scores (mean difference 7.9 points, p < 0.001).ConclusionsThe AI-IRIS improved efficiency and user perception without compromising accuracy. These findings suggest potential to support safety-related counting processes and streamline workflows. Future research should assess its integration in real-world perioperative environments and across diverse instrument sets and pack configurations.