Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Abstract

The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

Keywords: Artificial intelligence; Machine learning; Orthopedics; Trauma; Neural network

Core Tip: Machine learning is currently applied to image-screening assistance, predictive analytics, and intraoperative robotics, specifically in the trauma orthopedics field. Artificial intelligence can be used in the emergency department of trauma centers as a screening tool and aid to orthopedists, helping them improve their sensitivity and specificity and help shorten their diagnosis time. In real-life practice, orthopedic surgeons consider various factors when making a prediction; that is why machine learning-based predictive models include features such as history and physical exam data, along with imaging results. Artificial intelligence application may be able to identify such patterns and increase the chance of optimum results.