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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

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

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

Frédérick Bruneault
Emanuele Neri
Sema Candemir
Filippo Vaccher
Francesco Mureddu
Sune Holm
Steven Umbrello
Alberto Signoroni
Leonardo Espinosa-Leal
Elisabeth Hildt
Andy Spezzatti
James Brusseau
Annette F. Stephansen
Cecilia Panigutti
Genevieve Fieux Castagnet
Davide Farina
Himanshi Allahabadi
Francesca Pratesi
Christina Cociancig
Serg Masis
Irmhild van Halem
Subrata Chakraborty
Brendan Parent
Thomas Frauenfelder
Alessio Gallucci
Lau Bee Theng
Nicoleta Cherciu
Guya Giuliani
Walter Osika
Luca Alessandro Cappellini
Aniek F. Markus
Francesca Lizzi
Charles Binkley
Vince I. Madai
Pedro A. Moreno-Sánchez
Ulrich Kühne
Jonas Bozenhard
Hanna Sormunen
Megan Coffee
Adarsh Srivastava
Geneviève Fieux-Castagnet
Emilie Wiinblad Mathez
Irene Ek
Georgios Kararigas
Giovanni Sartor
Roberto V. Zicari
Renee Wurth
Magnus Westerlund
Jarno Tuominen
Julia Amann
Isabelle Balot
Dennis Vetter
Andrea Beretta
Matiss Ozols
Adam Golda
Jesmin Jahan Tithi
Sebastien A. Krier
Mattia Savardi
Katso/Avaa
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(5).pdf (855.3Kb)
Lataukset: 

IEEE
doi:10.1109/TTS.2022.3195114
URI
https://ieeexplore.ieee.org/document/9845195
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022102463093
Tiivistelmä

Abstract—The paper's main contributions are twofold: 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 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 multi-regional 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 socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.

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