EZ-AR: an accessible AR application for multiple model visualization and real-time tracking in healthcare settings

Introduction Augmented Reality (AR) offers significant potential to enhance surgical precision, improve clinical training, and support intraoperative decision-making. However, developing personalized AR applications for head-mounted displays such as Microsoft HoloLens 2 typically requires advanced technical expertise. Additionally, identifying a robust and accessible method for registering virtual models to the real world remains an open challenge. These limitations hinder the widespread adoption of AR in healthcare, particularly among non-technical users. Methods This work presents EZ-AR , an “ easy AR” application for Microsoft HoloLens 2 that automates complex tasks such as configuration and deployment, while ensuring high reliability and smooth interaction. Ultimately, the system enables non-expert users to independently load and visualize personalized 3D models anchored to physical reference markers. The application can load files from local storage or automatically download them from Google Drive in seconds, displaying them in the physical environment using Vuforia or QR code (version 3) tracking. Once loaded, the system operates entirely offline, eliminating any intraoperative dependence on Internet access. The system's performance was assessed in terms of detection range and overall system accuracy under varying surface inclinations and viewing angles. Results A mean projection error of 3.3 ± 2.2 mm was obtained with QR code tracking and 3.9 ± 2.7 mm with Vuforia, both measured at a working distance compatible with typical surgical ergonomics. Further analysis revealed that accuracy is more influenced by viewing angle and surface inclination than by the tracking method itself. Additionally, a usability survey conducted with clinicians confirmed the framework's high acceptability and clinical feasibility. Discussion Overall, EZ-AR provides a robust and accessible framework that simplifies the deployment of head-mounted AR, facilitating its translation and adoption in clinical workflows.