**5.1 Application in image interpretation**

Errors in image interpretation in trauma radiology can increase morbidity and mortality, and it has been estimated that there can be up to a 4% error rate even by a trained radiologist [10]. There is an increasing pressure on physicians to interpret radiological images due to their growing use. It has been estimated that the greatest number of undiagnosed fractures occur in patients assessed between 8:00 p.m. and 2:00 a.m. This is probably because physicians who can assess these images may not be available in certain facilities or at certain times of the day [11].

The application of an AI in the world of radiology is the natural consequence of history and discipline, which has been characterized by incorporating technological innovation into clinical practice [12]. However, most existing algorithms used to identify fractures usually provide performance similar to, but not superior to, the capabilities of an expert radiologist. Therefore, it is possible that physicians who are not specialists in musculoskeletal radiology may benefit the most from using these AI tools. For example, CNNs have been used to detect fractures on radiographs in different anatomic locations, including the upper extremity, lower extremity, hip, and spine [13].

On the other hand, AI-based imaging systems are usually used in specific anatomical locations, so they should be integrated with each other to have an impact on clinical practice. An example of this would be a study in which 715,343 radiographs from 16 anatomical sites and 10 CNNs were used to detect fractures with promising results [14]. Another example would be the use of DL on computed tomography (CT) images to detect osteoporotic femoral neck, calcaneal, and vertebral fractures with an acceptable result [15]. An interesting aspect is the ability of the AI to detect fractures that are inconspicuous to the human eye. An algorithm with the ability to detect subtle lesions might not be able to discover radiographically obvious fractures [16].

Algorithms have also been used to detect anterior cruciate ligament tears, finding no difference in sensitivity or specificity versus expert radiologists [17]. AI has also shown good results for diagnosing meniscal tears [18]. DL has also been used to evaluate acute and chronic cartilage lesions [19].
