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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. / Allahabadi, Himanshi ; Amann, Julia; Balot, Isabelle; Beretta, Andrea; Binkley, Charles; Bozenhard, Jonas; Bruneault, Frédérick ; 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; Halem, Irmhild van ; Hildt, Elisabeth; Holm, Sune ; Kararigas, Georgios ; Krier, Sebastien A.; Kühne, 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-Sánchez, Pedro A. ; Sartor, Giovanni; Savardi, Mattia; Signoroni, Alberto; Sormunen, Hanna; 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. .
In:
IEEE Transactions on Technology and Society, Vol. 3, No. 4, 2022, p. 272-289.
Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
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, Halem, IV, Hildt, E
, Holm, S, Kararigas, G, Krier, SA, Kühne, 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-Sánchez, PA, Sartor, G, Savardi, M, Signoroni, A, Sormunen, H, 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 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.2022.3195114APA
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 multi-regional 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.2022.3195114Vancouver
Allahabadi H, Amann J, Balot I, Beretta A, Binkley C, Bozenhard J et al.
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. 2022;3(4):272-289.
https://doi.org/10.1109/TTS.2022.3195114Author
Allahabadi, Himanshi ; Amann, Julia ; Balot, Isabelle ; Beretta, Andrea ; Binkley, Charles ; Bozenhard, Jonas ; Bruneault, Frédérick ; 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 ; Halem, Irmhild van ; Hildt, Elisabeth ; Holm, Sune ; Kararigas, Georgios ; Krier, Sebastien A. ; Kühne, 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-Sánchez, Pedro A. ; Sartor, Giovanni ; Savardi, Mattia ; Signoroni, Alberto ; Sormunen, Hanna ; 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. . / 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. In: IEEE Transactions on Technology and Society. 2022 ; Vol. 3, No. 4. pp. 272-289.
Bibtex
@article{3e8ccf06bdb44a30ab0f4148e7bc6b42,
title = "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",
abstract = "The paper{\textquoteright}s main contributions are twofold: to demonstrate how to apply the general European Union{\textquoteright}s High-Level Expert Group{\textquoteright}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{\textquoteright}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.",
author = "Himanshi Allahabadi and Julia Amann and Isabelle Balot and Andrea Beretta and Charles Binkley and Jonas Bozenhard and Fr{\'e}d{\'e}rick 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 Halem, {Irmhild van} and Elisabeth Hildt and Sune Holm and Georgios Kararigas and Krier, {Sebastien A.} and Ulrich K{\"u}hne 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-S{\'a}nchez, {Pedro A.} and Giovanni Sartor and Mattia Savardi and Alberto Signoroni and Hanna Sormunen 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.}",
year = "2022",
doi = "10.1109/TTS.2022.3195114",
language = "English",
volume = "3",
pages = "272--289",
journal = "IEEE Transactions on Technology and Society",
number = "4",
}
RIS
TY - JOUR
T1 - Assessing Trustworthy AI in times of COVID-19
T2 - Deep Learning for predicting a multi-regional 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, Frédérick
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 - Halem, Irmhild van
AU - Hildt, Elisabeth
AU - Holm, Sune
AU - Kararigas, Georgios
AU - Krier, Sebastien A.
AU - Kühne, 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-Sánchez, Pedro A.
AU - Sartor, Giovanni
AU - Savardi, Mattia
AU - Signoroni, Alberto
AU - Sormunen, Hanna
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
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1109/TTS.2022.3195114
DO - 10.1109/TTS.2022.3195114
M3 - Journal article
C2 - 36573115
VL - 3
SP - 272
EP - 289
JO - IEEE Transactions on Technology and Society
JF - IEEE Transactions on Technology and Society
IS - 4
ER -
