Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

Authors: Allahabadi, Himanshi, Amann, Julia, Balot, Isabelle, Beretta, Andrea, Binkley, Charles, Bozenhard, Jonas, Bruneault, Frederick, Brusseau, James, Candemir, Sema, Cappellini, Luca Alessandro, Chakraborty, Subrata, Cherciu, Nicoleta, Cociancig, Christina, Coffee, Megan, Ek, Irene, Espinosa-Leal, Leonardo, Farina, Davide, Fieux-Castagnet, Genevieve, Frauenfelder, Thomas, Gallucci, Alessio, Giuliani, Guya, Golda, Adam, van Halem, Irmhild, Hildt, Elisabeth, Holm, Sune, Kararigas, Georgios, Krier, Sebastien A., Kuhne, Ulrich, Lizzi, Francesca, Madai, Vince I., Markus, Aniek F., Masis, Serg, Mathez, Emilie Wiinblad, Mureddu, Francesco, Neri, Emanuele, Osika, Walter, Ozols, Matiss, Panigutti, Cecilia, Parent, Brendan, Pratesi, Francesca, Moreno-Sanchez, Pedro A., Sartor, Giovanni, Savardi, Mattia, Signoroni, Alberto, Sormunen, Hanna-Maria, Spezzatti, Andy, Srivastava, Adarsh, Stephansen, Annette F., Theng, Lau Bee, Tithi, Jesmin Jahan, Tuominen, Jarno, Umbrello, Steven, Vaccher, Filippo, Vetter, Dennis, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V.

Source: IEEE Transactions on Technology and Society. 3(4):272-289

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