Bone fractures are among the most common causes of emergency department visits. Diagnostic errors often occur due to misinterpretation of radiological examination, which may lead to the delayed treatment and poor outcomes. [1] The analysis of causes of fracture diagnostic inaccuracies has found them to be multifactorial, including physician factors, image quality, insufficient clinical information, fracture type, and polytrauma. [2] Four out of five diagnostic errors in an emergency settings are due to physician factors, yet radiographs are often interpreted by clinicians who lack the required specialized expertise. [3] Even with an experienced radiologist, physician fatigue and error may increase during a long busy day, increasing the risk of missing a subtle fracture. [4] Thus, a model that can offer assistance to physicians presenting second opinions through highlighting concerning areas in imaging examination may produce more efficient interpretation, standardize quality, and decrease errors. With recent advances in deep learning (DL) and computer vision, artificial intelligence (AI) may play a significant role in this field.AI is a powerful technology that has demonstrated good potential at radiographic image interpretation. While earlier levels of AI performance were subhuman, modern versions are able to match or even surpass humans' performance. [5] AI has also shown promising results in complex diagnostics in other medical specialties such as ophthalmology, dermatology, and pathology. [6] The aim of this article is to explore the potential of utilizing AI in fracture diagnosis by reviewing the current literature on this subject.
technIcal aspectsAI, machine learning (ML), DL, and convolutional neural networking (CNN) are terminology, which often used interchangeably [Figure 1]. AI refers to any skill where a machine performs tasks that mimic human intelligence. ML is a subfield of AI that enables a machine to learn and improve from the experience independently of human action. DL is a more specialized subfield of ML, which can analyze more data sets transforming the inputs of an algorithm into outputs using