# Artificial Intelligence and Augmented Reality in Orthopedic Surgery: A Narrative Review of Current Applications and Future Directions

## TL;DR

AI and AR are being used in orthopedic surgery to improve imaging, planning, navigation, and education, but more research is needed for widespread adoption.

## Contribution

This paper reviews current AI and AR applications in orthopedic surgery and outlines future research directions for broader clinical adoption.

## Key findings

- AI techniques like machine learning and computer vision improve imaging accuracy and surgical planning.
- AR systems enhance real-time visualization and reduce fluoroscopy use in arthroplasty and spine surgery.
- Educational AR applications improve trainee performance and shorten learning curves.

## Abstract

Artificial intelligence (AI) and augmented reality (AR), including mixed reality systems, are increasingly integrated across the orthopedic surgical continuum to enhance imaging interpretation, preoperative planning, intraoperative navigation, postoperative assessment, and surgical education. AI techniques such as machine learning, deep learning, and computer vision now facilitate automated segmentation, three-dimensional reconstruction, radiographic measurement, implant identification, and risk prediction with high accuracy across multiple subspecialties. AR platforms provide real-time visualization of anatomy, component alignment, and instrument trajectories, with studies in arthroplasty and spine surgery demonstrating placement accuracy within 1-2 degrees or a few millimeters, along with reduced fluoroscopy usage in selected procedures. Applications in trauma, oncology, pediatrics, and sports medicine similarly show feasibility for improving visualization, optimizing implant planning, and supporting complex resections. Educational uses of AR and mixed reality consistently improve technical performance and shorten learning curves among trainees. Despite these advances, most published studies involve small single-center cohorts, heterogeneous outcome measures, and limited external validation of AI models. AR systems also face practical challenges related to ergonomics, workflow integration, registration accuracy, and device usability. Overall, current evidence indicates that AI and AR have progressed from experimental concepts to early clinical utility across several orthopedic domains; however, broader adoption will require multicenter studies powered for patient-centered and economic outcomes, standardized reporting and validation of AI models, rigorous human-factors evaluation of AR interfaces, and development of interoperable platforms that integrate real-time AI analytics into ergonomic AR workflows. These efforts are essential to ensure the safe, effective, and equitable implementation of AI- and AR-enhanced orthopedic care.