Current understanding on artificial intelligence and machine learning in orthopaedics
Abstract
A scoping review of 40 studies mapping the landscape of AI in orthopedics. The paper categorizes AI applications into diagnostic imaging, implant templating, and robotic navigation, identifying Deep Learning as the current dominant methodology.
The Digital Orthopedic Revolution
Key Finding
"Artificial Intelligence models, particularly Convolutional Neural Networks (CNNs), achieved diagnostic accuracy rates comparable to senior sub-specialists in fracture detection and implant sizing."
Background: Orthopedics is data-dense but analysis-poor. The surge in computing power has birthed AI tools capable of reading radiographs and predicting surgical outcomes. However, the clinician is currently flooded with hype. This review cuts through the noise to define exactly where AI adds clinical value versus where it remains experimental.
Methods: The authors conducted a scoping review of 40 articles selected from PubMed, Scopus, and EMBASE. The study strictly excluded non-clinical engineering papers to focus on clinically translational technologies. Applications were stratified into trauma (fracture detection), spine (deformity analysis), and arthroplasty (implant positioning and robotics).
Results: Deep Learning (DL) and Machine Learning (ML) are transforming diagnostics: AI algorithms consistently identified subtle fractures (e.g., scaphoid, hip) with sensitivity exceeding 90%. In arthroplasty, AI-driven pre-operative templating reduced implant size mismatch. However, the review highlights a critical translation gap—most models lack external validation on diverse patient populations, limiting their immediate plug-and-play utility in routine practice.
Conclusion: AI is not a replacement for the surgeon but a high-precision second opinion. The immediate future of orthopedic AI lies in automated triage of trauma radiographs and patient-specific surgical planning, moving from experimental novelty to standard-of-care utility.