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In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as case identification or monitoring of the population. The urgency of the situation should not be to the detriment of considering the ethical implications of such apps. Implementing ethics in medical AI is a complex issue calling for a systems thinking approach engaging diverse representatives of the stakeholders in a consultative process. The participatory engagement aims to gather the different perspectives of the stakeholders about the app in a transparent and inclusive way. In this study, we engaged a group of clinicians, patients, and AI developers in conversations about a fictitious app which was an aggregate of actual COVID-19 apps. The app featured a COVID-19 symptoms monitoring function for both the patient and the clinician, as well as infection clusters tracking for health agencies. Anchored in Soft Systems Methodology and Critical Systems Thinking, participants were asked to map the flow of knowledge between the clinician, the patient, and the AI app system and answer questions about the ethical boundaries of the system. Because data and information are the resource and the product of the AI app, understanding the nature of the information and knowledge exchanged between the different agents of the system can reveal ethical issues. In this study, not only the output of the participatory process was analysed, but the process of the stakeholders' engagement itself was studied as well. To establish a strong foundation for the implementation of ethics in the AI app, the conversations among stakeholders need to be inclusive, respectful and allow for free and candid dialogues ensuring that the process is transparent for which a systemic intervention is well suited., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Goirand et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have been successful. AI specific ethics frameworks in healthcare appear to have a limited adoption and they are mostly used in conjunction with other ethics frameworks. The operationalisation of ethics frameworks is a complex endeavour with challenges at different levels: ethics principles, design, technology, organisational, and regulatory. Strategies identified in this review are proactive, contextual, technological, checklist, organisational and/or evidence-based approaches. While interdisciplinary approaches show promises, how an ethics framework is implemented in an AI-based Healthcare Application is not widely reported, and there is a need for transparency for trustworthy AI., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Introduction: While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. While views on the benefits of AI in healthcare vary, the ontological and epistemic consequences of AIHA on its users are likely to be significant. For example, AIHA has potential to reshape the patient-doctor relationship. Understanding how ethics are implemented in AIHA is critical, yet it is still unclear how it is done. We will present the findings of a review that explored how ethics have been operationalised in AIHA. Aims Objectives Theory or Methods: This review aimed to survey how AI ethics frameworks have been implemented and evaluated in healthcare applications, and map the scope, challenges and practices of these initiatives. We conducted a systematic scoping review, searching OvidMEDLINE, Scopus, EMBASE, Web of Science and grey literature related to the implementation of ethics frameworks in AI applications in healthcare published between 2015 and 2020. This scoping review followed the Joanna Briggs Institute's (JBI) guidance for systematic scoping reviews and the preferred reporting items for systematic reviews and meta-analyses statement (PRISMA). Highlights or Results or Key Findings: 4444 peer-reviewed and grey literature articles were title and abstract screened, 480 underwent a full-text review. Narrative synthesis was performed on the 33 included articles. AIHA included Clinical Decisions Support Systems, drones, and Intelligent Assistive Technologies, such as virtual bots and robots. Operationalising ethics frameworks is a complex endeavour with challenges at different levels: ethics principles, design, technology, organisational, and regulatory, which calls for an interdisciplinary approach to AIHA. Among identified strategies, some offered proactive, contextual and organisational approaches involving stakeholders' consultations. Crucially, the balance of power between producers and users of AIHAs is unclear in the identified strategies and the decision-making process lacks transparency. While critical for achieving transparency and clarity, clear measures of success of the implementation of an ethics frameworks could not be found. Because the more widely adopted an AI system is, the safer it becomes, establishing trust in the system is a wicked issue. Conclusions: Operationalising an ethics framework in AIHA requires contextual, proactive and inclusive approaches. Transparency of the process is needed, especially in light of the power imbalance between the consumers and the providers. Cross-pollination between computer, medical and social sciences promises to yield the most comprehensive ways to tackle this wicked issue. Implications for applicability/transferability sustainability and limitations: There is a need to establish a dialogue for a shared meaning and understanding of AIHA to provide wholesome ethical foundations for its implementation. While the review strived to include grey literature, private sector initiatives are likely underrepresented. Because of the language barrier, Chinese, Korean and Japanese initiatives are likely underrepresented. [ABSTRACT FROM AUTHOR]