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

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

Zicari, Roberto V. (författare): Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland; Data Science Graduate School, Seoul National University, Seoul, South Korea

Brusseau, James (författare): Philosophy Department, Pace University, NY, New York, United States

Blomberg, Stig Nikolaj (författare): University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark

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Christensen, Helle Collatz (författare): University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark

Coffee, Megan (författare): Department of Medicine and Division of Infectious Diseases and Immunology, NYU Grossman School of Medicine, NY, New York, United States

Ganapini, Marianna B. (författare): Montreal AI Ethics Institute, Canada and Union College, NY, New York, United States

Gerke, Sara (författare): Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School, CA, Berkeley, United States

Gilbert, Thomas Krendl (författare): Center for Human-Compatible AI, University of California, CA, Berkeley, United States

Hickman, Eleanore (författare): Faculty of Law, University of Cambridge, Cambridge, United Kingdom

Hildt, Elisabeth (författare): Center for the Study of Ethics in the Professions, Illinois Institute of Technology Chicago, IL, Chicago, United States

Holm, Sune (författare): Department of Food and Resource Economics, Faculty of Science University of Copenhagen, Copenhagen, Denmark

Kühne, Ulrich (författare): Hautmedizin, Bad Soden, Germany

Madai, Vince I. (författare): CLAIM - Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany; QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany; School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, London, United Kingdom

Osika, Walter (författare): Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

Spezzatti, Andy (författare): Industrial Engineering and Operation Research, University of California, CA, Berkeley, United States

Schnebel, Eberhard (författare): Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany

Tithi, Jesmin Jahan (författare): Parallel Computing Labs, Intel, CA, Santa Clara, United States

Vetter, Dennis (författare): Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany

Westerlund, Magnus (författare): Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland

Wurth, Renee (författare): Fitbiomics, NY, New York, United States

Amann, Julia (författare): Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland

Antun, Vegard (författare): Department of Mathematics, University of Oslo, Oslo, Norway

Beretta, Valentina (författare): Department of Economics and Management, Università degli studi di Pavia, Pavia, Italy

Bruneault, Frédérick (författare): École des médias, Université du Québec à Montréal and Philosophie, Collège André-Laurendeau, QC, Québec, Canada

Campano, Erik (författare): Umeå universitet,Institutionen för informatik

Düdder, Boris (författare): Department of Computer Science (DIKU), University of Copenhagen (UCPH), Copenhagen, Denmark

Gallucci, Alessio (författare): Department of Mathematics and Computer Science Eindhoven University of Technology, Eindhoven, Netherlands

Goffi, Emmanuel (författare): Observatoire Ethique and Intelligence Artificielle de l’Institut Sapiens, Paris, Cachan, France

Haase, Christoffer Bjerre (författare): Section for Health Service Research and Section for General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

Hagendorff, Thilo (författare): Cluster of Excellence "Machine Learning: New Perspectives for Science", University of Tuebingen, Tuebingen, Germany

Kringen, Pedro (författare): Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany

Möslein, Florian (författare): Institute of the Law and Regulation of Digitalization, Philipps-University Marburg, Philipps, Germany

Ottenheimer, Davi (författare): Inrupt, CA, San Francisco, United States

Ozols, Matiss (författare): University of Manchester and Wellcome Sanger Institute, Cambridge, United Kingdom

Palazzani, Laura (författare): Philosophy of Law, LUMSA University, Rome, Italy

Petrin, Martin (författare): Law Department, Western University, ON, London, Canada; Faculty of Laws, University College London, London, United Kingdom

Tafur, Karin (författare): Law and Ethics) and Legal Tech Entrepreneur, Barcelona, Spain

Tørresen, Jim (författare): Department of Informatics, University of Oslo, Oslo, Norway

Volland, Holger (författare): Head of Community and Communications, Z-Inspection® Initiative, london, United Kingdom

Kararigas, Georgios (författare): Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

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2021-07-08
2021
Engelska.
Ingår i: Frontiers in Human Dynamics. - : Frontiers Media S.A.. - 2673-2726. ; 3

Relaterad länk:: https://doi.org/10.3...; visa fler...; https://umu.diva-por... (primary) (Raw object); https://www.frontier...; https://urn.kb.se/re...; https://doi.org/10.3...; visa färre...

Tidskriftsartikel (refereegranskat)

Abstract Ämnesord

Stäng

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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Health Care Service and Management, Health Policy and Services and Health Economy (hsv//eng)
NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

artificial intelligence
cardiac arrest
case study
ethical trade-off
explainable AI
healthcare
trust
trustworthy AI

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MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Hälsovetenskap; och Hälso och sjukvå ...

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Av lärosätet: Umeå universitet

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