**9. COVID-19 research landscape remodeling**

The COVID-19 pandemic created a compelling need for innovation in testing to generate solutions that were cheap, easy to use, fast, and ubiquitous. Since lung imaging is a useful diagnostic tool, during the pandemic many research groups began to look for solutions using AI and computer vision [125]. As lung imaging is an important resource in emergency medicine for optimal triage of patients with suspected COVID-19 infection, computer vision solutions aimed to be a rapid analysis element that could speed up patient management times. From 2019 to 2020, a nearly two-fold increase in the number of publications on the artificial intelligence applied to medical imaging was observed. Moreover, starting from zero publications in 2019, by 2020 about 15% of all deep learning research associated with medical imaging was on COVID-19. With respect to the focus on the type of medical imaging, it was observed that of all the proposed computer vision solutions, almost half (49.7%) were focused only on X-rays. The remaining modalities were CT (38.7%), multimodality (10.2%), and ultrasonography (1.5%) [125]. As the research progressed, the usefulness of ultrasound as a tool for the diagnosis and management of COVID-19 was also observed. The ease of maintaining sterility, the possibility of performing bedside operations, the reduced time to obtain the image, and the possibility of using only one operator for the procedure have made this imaging modality highly suitable for this pandemic. The group of Born et al. opened the door to the use of deep learning with ultrasound for COVID-19 screening [126, 127]. Several groups followed with different proposals and today, the field has grown considerably by extending applications to other pathologies [128, 129].
