Digital neurosurgery in the era of intelligent medicine: a scoping review.

Abstract

<h4>Background</h4>Digital neurosurgery represents a transformative shift in modern neurosurgical practice, integrating advanced technologies, such as three-dimensional (3D) imaging, robotics, artificial intelligence (AI), and digital twin technology (DTT) models. These technologies offer innovative solutions for preoperative planning, intraoperative navigation, and postoperative management, with an emphasis on precision, personalization, and efficiency.<h4>Methods</h4>We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) checklist and guidance from the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. PubMed, Web of Science (WOS), and China National Knowledge Infrastructure (CNKI) were searched without language or date limits through September 2025. Eligibility was structured using the Population-Concept-Context (PCC) framework. Two reviewers independently screened records in Rayyan with consensus resolution, and data were charted using a prepiloted form. A total of 133 sources were included and mapped.<h4>Results</h4>Key technologies reviewed include: (1) 3D reconstruction: Facilitates precise anatomical modeling, improving spatial understanding and surgical planning. (2) 3D printing (3DP): Enables creation of patient-specific models and surgical guides, enhancing preoperative simulation and intraoperative accuracy. (3) Digital twins (DT): Offers dynamic virtual models for real-time surgical simulation, training, and personalized patient management. (4) Intraoperative navigation: Utilizes advanced electromagnetic and AI-enhanced systems to improve tracking accuracy and reduce surgical errors. (5) Robotic-assisted surgery: Includes telesurgical, supervisory, and handheld systems that enhance precision and enable minimally invasive procedures. (6) AI: Supports image registration, subtask automation, and clinical decision-making, improving diagnostic and prognostic accuracy. These technologies demonstrate significant benefits in operative precision, patient outcomes, training efficacy, and interdisciplinary communication, though challenges remain in data integration, regulatory standards, and computational demands.<h4>Conclusion</h4>Positioning this study as a scoping review clarifies its objective to map technologies and applications across digital neurosurgery rather than to synthesize effect estimates, thereby providing an evidence-informed overview to guide future systematic evaluations. Digital neurosurgery is rapidly evolving toward greater integration of multimodal data, real-time adaptive systems, and AI-driven automation. Future developments should focus on standardizing regulatory frameworks, enhancing data fusion capabilities, and promoting interdisciplinary collaboration to fully realize the potential of digital technologies in advancing neurosurgical care.