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On assessing trustworthy AI in healthcare: Machine learning as a supportive tool to recognize cardiac arrest in emergency calls
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.
University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark.
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2021 (English)In: Frontiers in Human Dynamics, E-ISSN 2673-2726 , Vol. 3, article id 673104Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2021. Vol. 3, article id 673104
Keywords [en]
artificial intelligence, cardiac arrest, case study, ethical trade-off, explainable AI, healthcare, trust, trustworthy AI
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-217548DOI: 10.3389/fhumd.2021.673104ISI: 001094060300001Scopus ID: 2-s2.0-85177815584OAI: oai:DiVA.org:umu-217548DiVA, id: diva2:1819211
Funder
Novo Nordisk Foundation, NNF17SA0027784EU, Horizon 2020, 777107German Research Foundation (DFG), EXC 2064/ 1Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-19Bibliographically approved

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Campano, Erik

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