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

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

doi:10.1109/TTS.2022.3195114

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

Himanshi Allahabadi
, Julia Amann
, Isabelle Balot
, Andrea Beretta
, Charles Binkley
, Jonas Bozenhard
, Frederick Bruneault
, James Brusseau
, Sema Candemir
, Luca Alessandro Cappellini
, Subrata Chakraborty
, Nicoleta Cherciu
, Christina Cociancig
, Megan Coffee
, Irene Ek
, Leonardo Espinosa-Leal
, Davide Farina
, Genevieve Fieux-Castagnet
, Thomas Frauenfelder
, Alessio Gallucci

Guya Giuliani, Adam Golda, Irmhild Van Halem, Elisabeth Hildt, Sune Holm, Georgios Kararigas, Sebastien A. Krier, Ulrich Kuhne, Francesca Lizzi, Vince I. Madai, Aniek F. Markus, Serg Masis, Emilie Wiinblad Mathez, Francesco Mureddu, Emanuele Neri, Walter Osika, Matiss Ozols, Cecilia Panigutti, Brendan Parent, Francesca Pratesi, Pedro A. Moreno-Sanchez, Giovanni Sartor, Mattia Savardi, Alberto Signoroni, Hanna Maria Sormunen, Andy Spezzatti, Adarsh Srivastava, Annette F. Stephansen, Lau Bee Theng, Jesmin Jahan Tithi, Jarno Tuominen, Steven Umbrello, Filippo Vaccher, Dennis Vetter, Magnus Westerlund, Renee Wurth, Roberto V. Zicari^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	272-289
Number of pages	18
Journal	IEEE Transactions on Technology and Society
Volume	3
Issue number	4
DOIs	https://doi.org/10.1109/TTS.2022.3195114
Publication status	Published - 1 Dec 2022
Externally published	Yes

Keywords

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

Access to Document

10.1109/TTS.2022.3195114

Cite this

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., ... Zicari, R. V. (2022). 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. IEEE Transactions on Technology and Society, 3(4), 272-289. https://doi.org/10.1109/TTS.2022.3195114

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title = "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",

abstract = "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{\textregistered}, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.",

keywords = "Artificial intelligence, COVID-19, Z-Inspection{\^A}{\textregistered}, case study, ethical tradeoff, ethics, explainable AI, healthcare, pandemic, radiology, trust, trustworthy AI",

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

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2022",

month = dec,

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doi = "10.1109/TTS.2022.3195114",

language = "English",

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journal = "IEEE Transactions on Technology and Society",

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Allahabadi, H, Amann, J, Balot, I, Beretta, A, Binkley, C, Bozenhard, J, Bruneault, F, Brusseau, J, Candemir, S, Cappellini, LA, 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, Van Halem, I, Hildt, E, Holm, S, Kararigas, G, Krier, SA, Kuhne, U, Lizzi, F, Madai, VI, Markus, AF, Masis, S, Mathez, EW, Mureddu, F, Neri, E, Osika, W, Ozols, M, Panigutti, C, Parent, B, Pratesi, F, Moreno-Sanchez, PA, Sartor, G, Savardi, M, Signoroni, A, Sormunen, HM, Spezzatti, A, Srivastava, A, Stephansen, AF, Theng, LB, Tithi, JJ, Tuominen, J, Umbrello, S, Vaccher, F, Vetter, D, Westerlund, M, Wurth, R & Zicari, RV 2022, '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', IEEE Transactions on Technology and Society, vol. 3, no. 4, pp. 272-289. https://doi.org/10.1109/TTS.2022.3195114

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. / Allahabadi, Himanshi; Amann, Julia; Balot, Isabelle et al.
In: IEEE Transactions on Technology and Society, Vol. 3, No. 4, 01.12.2022, p. 272-289.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Assessing Trustworthy AI in Times of COVID-19

T2 - Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

AU - Allahabadi, Himanshi

AU - Amann, Julia

AU - Balot, Isabelle

AU - Beretta, Andrea

AU - Binkley, Charles

AU - Bozenhard, Jonas

AU - Bruneault, Frederick

AU - Brusseau, James

AU - Candemir, Sema

AU - Cappellini, Luca Alessandro

AU - Chakraborty, Subrata

AU - Cherciu, Nicoleta

AU - Cociancig, Christina

AU - Coffee, Megan

AU - Ek, Irene

AU - Espinosa-Leal, Leonardo

AU - Farina, Davide

AU - Fieux-Castagnet, Genevieve

AU - Frauenfelder, Thomas

AU - Gallucci, Alessio

AU - Giuliani, Guya

AU - Golda, Adam

AU - Van Halem, Irmhild

AU - Hildt, Elisabeth

AU - Holm, Sune

AU - Kararigas, Georgios

AU - Krier, Sebastien A.

AU - Kuhne, Ulrich

AU - Lizzi, Francesca

AU - Madai, Vince I.

AU - Markus, Aniek F.

AU - Masis, Serg

AU - Mathez, Emilie Wiinblad

AU - Mureddu, Francesco

AU - Neri, Emanuele

AU - Osika, Walter

AU - Ozols, Matiss

AU - Panigutti, Cecilia

AU - Parent, Brendan

AU - Pratesi, Francesca

AU - Moreno-Sanchez, Pedro A.

AU - Sartor, Giovanni

AU - Savardi, Mattia

AU - Signoroni, Alberto

AU - Sormunen, Hanna Maria

AU - Spezzatti, Andy

AU - Srivastava, Adarsh

AU - Stephansen, Annette F.

AU - Theng, Lau Bee

AU - Tithi, Jesmin Jahan

AU - Tuominen, Jarno

AU - Umbrello, Steven

AU - Vaccher, Filippo

AU - Vetter, Dennis

AU - Westerlund, Magnus

AU - Wurth, Renee

AU - Zicari, Roberto V.

PY - 2022/12/1

Y1 - 2022/12/1

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - COVID-19

KW - Z-InspectionÂ®

KW - case study

KW - ethical tradeoff

KW - ethics

KW - explainable AI

KW - healthcare

KW - pandemic

KW - radiology

KW - trust

KW - trustworthy AI

UR - https://www.scopus.com/pages/publications/105025434540

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DO - 10.1109/TTS.2022.3195114

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JO - IEEE Transactions on Technology and Society

JF - IEEE Transactions on Technology and Society

IS - 4

ER -

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

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Keywords

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