**5.3 Clinical workflow**

In general, AI systems have the potential to assist physicians in certain tasks by improving the ability to diagnose and treat accurately despite the increased workload. Within the radiological practice, AI could improve two very important aspects such as effectiveness and efficiency. Effectiveness implies accuracy in interpreting radiological images and taking optimal clinical action. On the other hand, efficiency implies the optimization of workflows to make the best use of available resources and avoid clinical errors. These benefits would be achieved even considering the increased care pressure on physicians nowadays and the enormous workload involved in imaging on modern musculoskeletal radiology machines [12].

AI can be used to optimize clinical workflow and prioritize the tasks to be performed by clinicians. For example, an algorithm would be able to analyze a queue of images pending assessment and determine those that should be reviewed earlier because they are more likely to be pathological. This could be a critical advance in emergency situations, such as reviewing brain scan images to rule out intracranial hemorrhage [23].

Furthermore, it could accelerate image acquisition. Algorithms have been used to obtain MRI scans in five minutes that have higher image quality than other conventional MRI scans and can be optimally assessed by specialist radiologists [24].
