دورية أكاديمية
On assessing trustworthy AI in healthcare:Best practice for machine learning as a supportive tool to recognize cardiac arrest in emergency calls
| العنوان: | On assessing trustworthy AI in healthcare:Best practice for machine learning as a supportive tool to recognize cardiac arrest in emergency calls |
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| المؤلفون: | Zicari, Roberto V., Brusseau, James, Blomberg, Stig Nikolaj, Christensen, Helle Collatz, Coffee, Megan, Ganapini, Marianna B., Gerke, Sara, Gilbert, Thomas Krendl, Hickman, Eleanore, Hildt, Elisabeth, Holm, Sune, Kühne, Ulrich, Madai, Vince I., Osika, Walter, Spezzatti, Andy, Schnebel, Eberhard, Tithi, Jesmin Jahan, Vetter, Dennis, Westerlund, Magnus, Wurth, Renee, Amann, Julia, Antun, Vegard, Beretta, Valentina, Bruneault, Frédérick, Campano, Erik, Düdder, Boris, Gallucci, Alessio, Goffi, Emmanuel, Haase, Christoffer Bjerre, Hagendorff, Thilo, Kringen, Pedro, Möslein, Florian, Ottenheimer, Davi, Ozols, Matiss, Palazzani, Laura, Petrin, Martin, Tafur, Karin, Tørresen, Jim, Volland, Holger, Kararigas, Georgios |
| المصدر: | Zicari , R V , Brusseau , J , Blomberg , S N , Christensen , H C , Coffee , M , Ganapini , M B , Gerke , S , Gilbert , T K , Hickman , E , Hildt , E , Holm , S , Kühne , U , Madai , V I , Osika , W , Spezzatti , A , Schnebel , E , Tithi , J J , Vetter , D , Westerlund , M , Wurth , R , Amann , J , Antun , V , Beretta , V , .... |
| بيانات النشر: | Frontiers Media S.A. Umeå universitet, Institutionen för informatik Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland; Data Science Graduate School, Seoul National University, Seoul, South Korea Philosophy Department, Pace University, NY, New York, United States University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark Department of Medicine and Division of Infectious Diseases and Immunology, NYU Grossman School of Medicine, NY, New York, United States Montreal AI Ethics Institute, Canada and Union College, NY, New York, United States Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School, CA, Berkeley, United States Center for Human-Compatible AI, University of California, CA, Berkeley, United States Faculty of Law, University of Cambridge, Cambridge, United Kingdom Center for the Study of Ethics in the Professions, Illinois Institute of Technology Chicago, IL, Chicago, United States Department of Food and Resource Economics, Faculty of Science University of Copenhagen, Copenhagen, Denmark Hautmedizin, Bad Soden, Germany 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 Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Industrial Engineering and Operation Research, University of California, CA, Berkeley, United States Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany Parallel Computing Labs, Intel, CA, Santa Clara, United States Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland Fitbiomics, NY, New York, United States Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland Department of Mathematics, University of Oslo, Oslo, Norway École des médias, Université du Québec à Montréal and Philosophie, Collège André-Laurendeau, QC, Québec, Canada Department of Computer Science (DIKU), University of Copenhagen (UCPH), Copenhagen, Denmark Department of Mathematics and Computer Science Eindhoven University of Technology, Eindhoven, Netherlands Observatoire Ethique and Intelligence Artificielle de l’Institut Sapiens, Paris, Cachan, France Section for Health Service Research and Section for General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark New Perspectives for Science", University of Tuebingen, Tuebingen, Germany Institute of the Law and Regulation of Digitalization, Philipps-University Marburg, Philipps, Germany Inrupt, CA, San Francisco, United States University of Manchester and Wellcome Sanger Institute, Cambridge, United Kingdom Philosophy of Law, LUMSA University, Rome, Italy Law Department, Western University, ON, London, Canada; Faculty of Laws, University College London, London, United Kingdom Law and Ethics) and Legal Tech Entrepreneur, Barcelona, Spain Department of Informatics, University of Oslo, Oslo, Norway Head of Community and Communications, Z-Inspection® Initiative, london, United Kingdom Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland Department of Economics and Management, Università degli studi di Pavia, Pavia, Italy |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | droit, info |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2673-2726 |
| العلاقة: | https://curis.ku.dk/ws/files/273702873/fhumd_2021_673104_1.24.pdfTest; https://curis.ku.dk/portal/da/publications/on-assessing-trustworthy-ai-in-healthcareTest(c640d7bd-134b-406b-addd-948585f94b90).html |
| DOI: | 10.3389/fhumd.2021.673104 |
| الإتاحة: | https://doi.org/10.3389/fhumd.2021.673104Test https://curis.ku.dk/ws/files/273702873/fhumd_2021_673104_1.24.pdfTest https://curis.ku.dk/portal/da/publications/on-assessing-trustworthy-ai-in-healthcareTest(c640d7bd-134b-406b-addd-948585f94b90).html |
| حقوق: | undefined |
| رقم الانضمام: | edsbas.7E427D85 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 26732726 |
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| DOI: | 10.3389/fhumd.2021.673104 |