Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

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.

In IEEE transactions on technology and society

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

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

2022-Dec

Artificial intelligence, COVID-19, Z-Inspection®, case study, ethical tradeoff, ethics, explainable AI, healthcare, pandemic, radiology, trust, trustworthy AI