## 1 Introduction  
In recent years, advancements in machine learning and deep learning have significantly enhanced the development of different areas in the healthcare domain, providing powerful tools for improving diagnostic accuracy and workflow efficiency. Radiology, in particular, has been one of the medical subspecialties more affected by this recent development [1], with many different use cases regarding medical imaging [2, 3, 4] such as disease classification (disease vs. no disease), localization (using saliency maps or bounding boxes), segmentation (which can be from both normal and pathologic areas), and image registration (aligning images from different techniques such as computed tomography, CT, and magnetic resonance imaging, MRI), but also more clinically related ones such as worklist prioritization [5], test scheduling and protocoling, image quality assessment [6], structured reporting, monitoring (temporal tracking of diseases) [7], or even multimodality (a combination of images plus clinical EMR data) [8]. Most of the medical devices and algorithms approved by the FDA in 2020, 72%, were related to radiology [9]. Nevertheless, radiology suffers from an inherent lack of resources, due to the shortage of radiologists [10] and growing demand for medical imaging procedures; according to the WHO [11], in 2016, \"an estimated 3.6 billion diagnostic medical examinations, such as X-rays, are performed every year\", a number that \"continues to grow as more people access medical care\". Despite the number of recent techniques that has emerged in the daily workflow of radiology departments, such as ultrasound, computed tomography (CT), or magnetic resonance imaging (MRI), conventional radiology (X-ray) continues to be the most frequently performed imaging procedure [12], up to 75% in a radiology department [13], due to their lower cost, higher availability and accessibility, and great usefulness in the diagnosis of a broad range of conditions Figure 1.  
This has increased the pressure on radiology departments, generally without enough resources to focus on X-rays, which results in delays in report generation, clinical errors due to excessive workload, or even a direct interpretation from the referring physician of X-ray studies, without a proper radiological report [13], something widely acknowledged as leading to legal issues, as the formal reporting of radiological procedures is a professional duty mandated by the current regulations. There is not a simple solution to these challenges, as they are expected to continue rising, so it is essential that we explore strategies to mitigate them, and the implementation of artificial intelligence in radiology departments can provide the enhanced efficiency required to reduce this impact [14], for instance, by enabling early triage and the preliminary diagnosis of critical cases. In these situations, triaging using machine-learning tools can be highly effective in reducing diagnostic delays and avoiding a missed diagnosis. Triaging in medicine refers to the process of sorting and prioritizing patients based on the severity of their conditions. The ultimate goal is to optimize resource allocation and save lives by ensuring critical cases receive prompt attention. Several studies have focused recently on the application of deep learning to chest radiographs in the emergency department. [PERSON] et al. [15] developed an AI system for the real-time triaging of adult chest radiographs, classifying them in critical, urgent, nonurgent, or normal, with a high negative  
Figure 1: Most common radiological projections for chest X-rays. (**a**) Posteroanterior (PA) is the standard frontal projection. (**b**) Lateral (L) projection is generally taken in conjunction with the PA view. Both are taken in standing position and full inspiration. (**c**) Anteroposterior (AP) view is generally performed for patients with more severe diseases, as it can be performed using portable devices and in bed, outside of the radiology department.