Artificial Intelligence vs. Physician Expertise in Appendicular Skeleton Fracture Detection: A Scoping Review
Authors: Christoforides Elie , Rust Brandon , Mulroy Duncan , Oswald Samuel , Muralidhar Rohit 
Published: January 2025

Background
Fracture types, resulting from both acute and chronic conditions, vary widely, including closed, open, stress, and pathological fractures. Trauma, falls, and sports injuries are leading causes, particularly among younger males, while factors like age, gender, and bone density significantly influence fracture susceptibility. Artificial intelligence (AI), through machine learning (ML) and convolutional neural networks (CNN), has emerged as a powerful tool in fracture detection, improving diagnostic accuracy and reducing errors in interpretation. Radiological AI algorithms are increasingly used in clinical settings, offering reliable diagnostic performance on par with trained clinicians. . 

Purpose/Hypothesis
This review examines the role of AI in fracture detection, focusing on its accuracy compared to physician diagnoses and its potential to enhance clinical outcomes across different anatomical regions of the appendicular skeleton. 

Study/Design
The exploration includes the AI's role in enhancing clinical care, its comparative accuracy with trained clinicians, and its potential in minimizing diagnostic disparities during trauma-related scenarios.

Methods
A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language between January 1, 2018 to December 31, 2022. Inclusion criteria targeted studies utilizing artificial intelligence modalities, radiographic images of pediatric or adult fractures, and the participation of orthopedic surgeons or radiologists for comparison of diagnostic accuracy with AI.

Results
A total of 754 articles were screened, with 36 meeting inclusion criteria for this review. These studies focused on AI-based fracture detection compared directly with physician diagnoses, specifically for appendicular skeleton fractures. Among the included articles, 13 compared AI to radiologists, 3 to orthopedic surgeons, and 1 to ER physicians, with 19 involving unspecified or mixed specialties. AI models were categorized by type, with 12 using Convolutional Neural Networks (CNNs), 14 using Deep Convolutional Neural Networks (DCNNs), and 10 employing unspecified deep learning models. Regarding fracture locations, 5 studies focused on the distal radius, 4 on wrist bones, 2 each on ankle and humerus fractures, 4 on the scaphoid, 6 on the hip, and 3 on the femur, while 7 evaluated fractures across the appendicular skeleton. Most studies were retrospective cohort designs (n=35), with one prospective study included. A general population was examined in 28 studies, while 5 focused on pediatric fractures, and 1 excluded pediatric images. X-rays were the primary imaging modality in 35 studies, with 1 study using MRI and CT. Comparative results indicated that AI outperformed physicians in 7 studies, matched their performance in 10, and was outperformed by physicians in 1. Physician performance improved with AI assistance in 11 studies. Additionally, 8 studies compared AI to “ground-truth” diagnoses to assess sensitivity and specificity. This categorization highlights the range of AI model types, specialties, and comparison methods across the studies.

Conclusions
This review evaluates the application of AI in radiological fracture detection. While current AI algorithms demonstrate promising potential, our findings indicate a need for further improvement in predictive accuracy before broad clinical implementation. Multidisciplinary collaboration appears crucial for optimizing diagnostic outcomes and improving patient care.