On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

  • Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.

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Author:Roberto V. Zicari, James Brusseau, Stig Nikolaj Blomberg, Helle Collatz Christensen, Megan Coffee, Marianna B. Ganapini, Sara Gerke, Thomas Krendl Gilbert, Eleanore Hickman, Elisabeth Hildt, Sune Holm, Ulrich Kühne, Vince Istvan Madai, Walter Osika, Andy Spezzatti, Eberhard SchnebelORCiDGND, Jesmin Jahan Tithi, Dennis Vetter, Magnus Westerlund, Renee Wurth, Julia Amann, Vegard Antun, Valentina Beretta, Frédérick Bruneault, Erik Campano, Boris Düdder, Alessio Gallucci, Emmanuel Goffi, Christoffer Bjerre HaaseORCiD, Thilo Hagendorff, Pedro Kringen, Florian Möslein, Davi Ottenheimer, Matiss Ozols, Laura Palazzani, Martin Petrin, Karin Tafur, Jim Tørresen, Holger Volland, Georgios Kararigas
Parent Title (English):Frontiers in Human Dynamics
Publisher:Frontiers Media
Place of publication:Lausanne
Document Type:Article
Date of Publication (online):2021/07/08
Date of first Publication:2021/07/08
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2021/09/06
Tag:artificial intelligence; cardiac arrest; case study; ethical trade-off; explainable AI; healthcare; trust; trustworthy AI
Issue:art. 673104
Page Number:24
First Page:1
Last Page:24
SG was supported by a grant from the Collaborative Research Program for Biomedical Innovation Law, a scientifically independent collaborative research program supported by a Novo Nordisk Foundation grant (NNF17SA0027784). JA received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777107 (PRECISE4Q). TH was supported by the Cluster of Excellence “Machine Learning—New Perspectives for Science” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—Reference Number EXC 2064/1—Project ID 390727645. All other authors did not receive any funding (neither private nor public) to conduct this work.
Institutes:Medizin / Medizin
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoCreative Commons - Namensnennung 4.0