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Background: The inability to test at scale has become humanity's Achille'sheel in the ongoing war against the COVID-19 pandemic. A scalable screeningtool would be a game changer. Building on the prior work on cough-baseddiagnosis of respiratory diseases, we propose, develop and test an ArtificialIntelligence (AI)-powered screening solution for COVID-19 infection that isdeployable via a smartphone app. The app, named AI4COVID-19 records and sendsthree 3-second cough sounds to an AI engine running in the cloud, and returns aresult within two minutes. Methods: Cough is a symptom of over thirtynon-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19infection by cough alone an extremely challenging multidisciplinary problem. Weaddress this problem by investigating the distinctness of pathomorphologicalalterations in the respiratory system induced by COVID-19 infection whencompared to other respiratory infections. To overcome the COVID-19 coughtraining data shortage we exploit transfer learning. To reduce the misdiagnosisrisk stemming from the complex dimensionality of the problem, we leverage amulti-pronged mediator centered risk-averse AI architecture. Results: Resultsshow AI4COVID-19 can distinguish among COVID-19 coughs and several types ofnon-COVID-19 coughs. The accuracy is promising enough to encourage alarge-scale collection of labeled cough data to gauge the generalizationcapability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool.Instead, it offers a screening tool deployable anytime, anywhere, by anyone. Itcan also be a clinical decision assistance tool used to channelclinical-testing and treatment to those who need it the most, thereby savingmore lives.

AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App