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

Himanshi  Allahabadi; Julia Amann; Isabelle Balot; Andrea Beretta; Charles Binkley; Jonas Bozenhard; Frédérick  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 Kühne; 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-Sánchez; Giovanni Sartor; Mattia Savardi; Alberto Signoroni; Hanna 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 multi-regional score conveying the degree of lung compromise in COVID-19 patients

Himanshi Allahabadi, Julia Amann, Isabelle Balot, Andrea Beretta, Charles Binkley, Jonas Bozenhard, Frédérick 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 GallucciGuya Giuliani, Adam Golda, Irmhild van Halem, Elisabeth Hildt, Sune Holm, Georgios Kararigas, Sebastien A. Krier, Ulrich Kühne, 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-Sánchez, Giovanni Sartor, Mattia Savardi, Alberto Signoroni, Hanna 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

Section for Consumption, Bioethics and Governance

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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.

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

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10.1109/TTS.2022.3195114Licence: CC BY-NC-ND

Assessing Trustworthy AI in Times of COVID-19_ Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 PatientsFinal published version, 846 KBLicence: CC BY-NC-ND

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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.2022.3195114

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

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.2022.3195114

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.

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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.",

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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

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AU - Kararigas, Georgios

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AU - Lizzi, Francesca

AU - Madai, Vince I.

AU - Markus, Aniek F.

AU - Masis, Serg

AU - Mathez, Emilie Wiinblad

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AU - Osika, Walter

AU - Ozols, Matiss

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AU - Parent, Brendan

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AU - Spezzatti, Andy

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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.

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