LAUSR

Since late 2019, when emerged from Wuhan, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encircled the globe originating the coronavirus disease 2019 (COVID-19) pandemic (1). Up to December 5, it infected 65million people, and it caused 1.5 million deaths (2).While 80% of patients present mild symptoms, 20% may experience more severe symptoms that require strict follow-up and hospitalization. Further, about 28–30% of these hospitalized patients will be admitted to the intensive care unit (ICU) (3). COVID-19 burdened healthcare systems worldwide and changed the paradigm of providing patient care to maximize efficiency and prevent staff members’ transmission, which might decrease the workforce to manage the surge. The use of Artificial Intelligence (AI) gained attention during the COVID pandemic; there are many examples, including the use of mathematical modeling to understand the disease epidemiology, tracking cases, or even supporting decision-makers in pandemic planning (4–6). It’s worth mentioning the case of The Center for Systems Science and Engineering at Johns Hopkins University, which created the “Coronavirus Tracker R ” platform, unique evidence of the power of AI collecting and analyzing large amounts of data to track the pandemic progression worldwide (7). The data pipelines employed in AI in healthcare include steps of data gathering and processing, application of machine learning (ML) methods, and performance validation, with further translation into clinical applications with medical feedback, for example, in medical imaging. These pipelines could assist a large number of diagnostic tests and procedures performed by humans, which impact resource allocation, timing, and outcome prediction (Figure 1). Besides accelerating and improving processes, the application of AI methods could improve outcomes and reduce costs healthcare value chain. This had never been so relevant as in the COVID-19 pandemic. This piece aims to reflect on the value of AI during the COVID-19 pandemic, using the case of developments in medical imaging and electronic health data management.