Bridging Pre-Operative Planning and Intra-Operative Decision-Making: A Systematic Review of Information-Management Strategies in AR-Enhanced Surgery
Year: 2025
Type: review
Abstract: Augmented reality (AR) is transforming surgical practice by integrating pre-operative planning with intra-operative decision-making. This systematic review critically evaluates AR systems that bridge these phases through information-management strategies, focusing on real-time data fusion, contextual access to patient history, and mitigation of technical challenges. Following PRISMA 2020 guidelines, we analyzed 10 studies (from 2,209 screened records) that met inclusion criteria, spanning clinical deployments, cadaver trials, and simulated environments. Three dominant strategies emerged: (1) holistic multimodal + EHR integration (exemplified by ARAS), achieving sub-50 ms latency and reducing cognitive load via conversational interfaces; (2) real-time imaging fusion without EHR access, yielding sub-5 mm overlay accuracy but requiring external data checks; and (3) conceptual frameworks proposing workflow-aware navigation with limited validation. Key findings demonstrate that systems combining spatial coherence, semantic interoperability, context-aware filtering, and hands-free input (e.g., ARAS) significantly enhance workflow continuity and surgeon confidence. However, persistent challenges include latency ceilings, vendor-locked data silos, and uneven outcome reporting—only two studies measured patient-level end-points. Cross-cutting observations highlight latency as the universal performance bottleneck and semantic interoperability as the linchpin for scalable integration. This review identifies four pillars for next-generation AR surgical systems and calls for standardized metrics to evaluate their clinical impact. While evidence supports AR’s potential to improve precision and efficiency, multicenter trials are needed to validate patient outcomes.
Source: InfoScience Trends
Authors: Sana Asadi Ashenaabad, Nasrin Lotfi, Danial Daryabeigi Salami, Paniz Negareshifard, Afshin Zarei
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7077055303