IEEE transactions on technology and society, 3(4), 272-289 Allahabadi, H, Amann, J, Balot, I, Beretta, A, Binkley, C, Bozenhard, J, Bruneault, F, Brusseau, J, Candemir, S, Cappellini, L A, Chakraborty, S, Cherciu, N, Cociancig, C, Coffee, M, Ek, I, Espinosa-Leal, L, Farina, D, Fieux-Castagnet, G, Frauenfelder, T, Gallucci, A, Giuliani, G, Golda, A, Halem, I V, Hildt, E, Holm, S, Kararigas, G, Krier, S A, Kühne, U, Lizzi, F, Madai, V I, Markus, A F, Masis, S, Mathez, E W, Mureddu, F, Neri, E, Osika, W, Ozols, M, Panigutti, C, Parent, B, Pratesi, F, Moreno-Sánchez, P A, Sartor, G, Savardi, M, Signoroni, A, Sormunen, H, Spezzatti, A, Srivastava, A, Stephansen, A F, Theng, L B, Tithi, J J, Tuominen, J, Umbrello, S, Vaccher, F, Vetter, D, Westerlund, M, Wurth, R & Zicari, R V 2022, ' Assessing Trustworthy AI in times of COVID-19 : Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients ', IEEE Transactions on Technology and Society, vol. 3, no. 4, pp. 272-289 . https://doi.org/10.1109/TTS.2022.3195114 IEEE Transactions on Technology and Society