Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier

Research output: Contribution to journalJournal articlepeer-review

Standard

Co-design of a trustworthy AI system in healthcare : Deep learning based skin lesion classifier. / Zicari, Roberto V. ; Ahmed, Sheraz; Amann, Julia; Braun, Stephan Alexander; Brodersen, John; Bruneault, Frédérick ; Brusseau, James; Campano, Erik; Coffee, Megan; Dengel, Andreas; Düdder, Boris; Gallucci, Alessio; Gilbert, Thomas Krendl ; Gottfrois, Philippe ; Goffi, Emmanuel; Haase, Christoffer Bjerre; Hagendorff, Thilo; Hickman, Eleanore ; Hildt, Elisabeth; Holm, Sune ; Kringen, Pedro; Kühne, Ulrich; Lucieri, Adriano; Madai, Vince I. ; Moreno-Sánchez, Pedro A.; Medlicott, Oriana; Ozols, Matiss; Schnebel, Eberhard; Spezzatti, Andy; Tithi, Jesmin Jahan ; Umbrello, Steven; Vetter, Dennis; Volland, Holger; Westerlund, Magnus; Wurth, Renee.

In: Frontiers in Human Dynamics , Vol. 3, 688152, 07.2021.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Zicari, RV, Ahmed, S, Amann, J, Braun, SA, Brodersen, J, Bruneault, F, Brusseau, J, Campano, E, Coffee, M, Dengel, A, Düdder, B, Gallucci, A, Gilbert, TK, Gottfrois, P, Goffi, E, Haase, CB, Hagendorff, T, Hickman, E, Hildt, E, Holm, S, Kringen, P, Kühne, U, Lucieri, A, Madai, VI, Moreno-Sánchez, PA, Medlicott, O, Ozols, M, Schnebel, E, Spezzatti, A, Tithi, JJ, Umbrello, S, Vetter, D, Volland, H, Westerlund, M & Wurth, R 2021, 'Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier', Frontiers in Human Dynamics , vol. 3, 688152. https://doi.org/10.3389/fhumd.2021.688152

APA

Zicari, R. V., Ahmed, S., Amann, J., Braun, S. A., Brodersen, J., Bruneault, F., Brusseau, J., Campano, E., Coffee, M., Dengel, A., Düdder, B., Gallucci, A., Gilbert, T. K., Gottfrois, P., Goffi, E., Haase, C. B., Hagendorff, T., Hickman, E., Hildt, E., ... Wurth, R. (2021). Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier. Frontiers in Human Dynamics , 3, [688152]. https://doi.org/10.3389/fhumd.2021.688152

Vancouver

Zicari RV, Ahmed S, Amann J, Braun SA, Brodersen J, Bruneault F et al. Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier. Frontiers in Human Dynamics . 2021 Jul;3. 688152. https://doi.org/10.3389/fhumd.2021.688152

Author

Zicari, Roberto V. ; Ahmed, Sheraz ; Amann, Julia ; Braun, Stephan Alexander ; Brodersen, John ; Bruneault, Frédérick ; Brusseau, James ; Campano, Erik ; Coffee, Megan ; Dengel, Andreas ; Düdder, Boris ; Gallucci, Alessio ; Gilbert, Thomas Krendl ; Gottfrois, Philippe ; Goffi, Emmanuel ; Haase, Christoffer Bjerre ; Hagendorff, Thilo ; Hickman, Eleanore ; Hildt, Elisabeth ; Holm, Sune ; Kringen, Pedro ; Kühne, Ulrich ; Lucieri, Adriano ; Madai, Vince I. ; Moreno-Sánchez, Pedro A. ; Medlicott, Oriana ; Ozols, Matiss ; Schnebel, Eberhard ; Spezzatti, Andy ; Tithi, Jesmin Jahan ; Umbrello, Steven ; Vetter, Dennis ; Volland, Holger ; Westerlund, Magnus ; Wurth, Renee. / Co-design of a trustworthy AI system in healthcare : Deep learning based skin lesion classifier. In: Frontiers in Human Dynamics . 2021 ; Vol. 3.

Bibtex

@article{5877f957ad784f0e97e20200b4c331f6,
title = "Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier",
abstract = "This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.",
author = "Zicari, {Roberto V.} and Sheraz Ahmed and Julia Amann and Braun, {Stephan Alexander} and John Brodersen and Fr{\'e}d{\'e}rick Bruneault and James Brusseau and Erik Campano and Megan Coffee and Andreas Dengel and Boris D{\"u}dder and Alessio Gallucci and Gilbert, {Thomas Krendl} and Philippe Gottfrois and Emmanuel Goffi and Haase, {Christoffer Bjerre} and Thilo Hagendorff and Eleanore Hickman and Elisabeth Hildt and Sune Holm and Pedro Kringen and Ulrich K{\"u}hne and Adriano Lucieri and Madai, {Vince I.} and Moreno-S{\'a}nchez, {Pedro A.} and Oriana Medlicott and Matiss Ozols and Eberhard Schnebel and Andy Spezzatti and Tithi, {Jesmin Jahan} and Steven Umbrello and Dennis Vetter and Holger Volland and Magnus Westerlund and Renee Wurth",
year = "2021",
month = jul,
doi = "10.3389/fhumd.2021.688152",
language = "English",
volume = "3",
journal = "Frontiers in Human Dynamics ",
issn = "2673-2726",
publisher = "Frontiers Media",

}

RIS

TY - JOUR

T1 - Co-design of a trustworthy AI system in healthcare

T2 - Deep learning based skin lesion classifier

AU - Zicari, Roberto V.

AU - Ahmed, Sheraz

AU - Amann, Julia

AU - Braun, Stephan Alexander

AU - Brodersen, John

AU - Bruneault, Frédérick

AU - Brusseau, James

AU - Campano, Erik

AU - Coffee, Megan

AU - Dengel, Andreas

AU - Düdder, Boris

AU - Gallucci, Alessio

AU - Gilbert, Thomas Krendl

AU - Gottfrois, Philippe

AU - Goffi, Emmanuel

AU - Haase, Christoffer Bjerre

AU - Hagendorff, Thilo

AU - Hickman, Eleanore

AU - Hildt, Elisabeth

AU - Holm, Sune

AU - Kringen, Pedro

AU - Kühne, Ulrich

AU - Lucieri, Adriano

AU - Madai, Vince I.

AU - Moreno-Sánchez, Pedro A.

AU - Medlicott, Oriana

AU - Ozols, Matiss

AU - Schnebel, Eberhard

AU - Spezzatti, Andy

AU - Tithi, Jesmin Jahan

AU - Umbrello, Steven

AU - Vetter, Dennis

AU - Volland, Holger

AU - Westerlund, Magnus

AU - Wurth, Renee

PY - 2021/7

Y1 - 2021/7

N2 - This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

AB - This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

U2 - 10.3389/fhumd.2021.688152

DO - 10.3389/fhumd.2021.688152

M3 - Journal article

VL - 3

JO - Frontiers in Human Dynamics

JF - Frontiers in Human Dynamics

SN - 2673-2726

M1 - 688152

ER -

ID: 274276062