Augmented reality (AR) in recent years has transitioned from an experimental concept to an increasingly integral tool within neurosurgical practice [1]. By overlaying computer-generated images into the user's field of view in real-time, AR enables users to interact with virtual and real-world objects in parallel, assisting in preoperative planning, intraoperative navigation and surgical education [1], [2]. The rapid progression of AR in recent years has been fueled by advancements in software, hardware, and computational power, refining the quality, accuracy and integration of AR-generated visuals into the surgical workflow [3], [4]. By elaborating preoperative imaging—such as MRI, CT, tractography, angiography, and ultrasound—and rendering the information available in the 2D/3D environment [5], AR increases informational accessibility, equipping surgeons with real-time, dynamic, and patient-specific anchored anatomical reconstructions. These visuals can either be projected through head mounted devices or operative microscopes, enabling the visualization of anatomical structures whilst not obstructing the visual field [1].

AR present innate advantages in neurosurgical training both in cranial and spinal surgery providing preoperative rehearsal opportunities and real-time intraoperative guidance [1], [6]. Beyond training, AR enables personalized, patient-specific intraoperative decision making, allowing surgeons to adapt strategies dynamically based on enhanced visual and data-driven insights available because of this enhanced visual field. As its clinical utility continues to expand, we AR has become an increasingly integral part of routine surgical workflows, in both higher and lower income settings [5], [7].

Recent years have seen a synergy between AR and artificial intelligence, integrating computational modelling within preoperative planning, intraoperative decision-making, and predicting postoperative outcomes [8], [9]. AR convergence with robotics, machine learning, and sensor-based navigational systems has further expanded its scope within different neurosurgical subspecialties.

Previous systematic reviews synthesized the evolving role and clinical trends of AR in neurosurgical practice up to 2021 [5], covering primarily early-stage and experimental applications. However, given the continued technological advancements and growing body of clinical evidence [10], [11], AR is now transitioning from an experimental tool to a standard neurosurgical device, and therefore an updated synthesis has become necessary to assess the how this has been reflected in the literature between 2022 to 2024, adding to the previous review from 2012 to 2021 [5].

This systematic review aims to provide a comprehensive evaluation of the state-of-the-art of AR in neurosurgery, exploring how the recent literature addresses existing challenges, builds-upon previous findings, and projects future directions. By categorizing studies based on geographical location and neurosurgical application, we aim to establish a clearer consensus of AR status as a staple neurosurgical tool, identify areas of increased growth and experimentation, and project future directions for this technology in neurosurgery.