The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, (...) defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to “responsibility” and “AI in healthcare”, and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders’ responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle. (shrink)

Mobile devices with health apps, direct-to-consumer genetic testing, crowd-sourced information, and other data sources have enabled research by new classes of researchers. Independent researchers, citizen scientists, patient-directed researchers, self-experimenters, and others are not covered by federal research regulations because they are not recipients of federal financial assistance or conducting research in anticipation of a submission to the FDA for approval of a new drug or medical device. This article addresses the difficult policy challenge of promoting the welfare and interests of (...) research participants, as well as the public, in the absence of regulatory requirements and without discouraging independent, innovative scientific inquiry. The article recommends a series of measures, including education, consultation, transparency, self-governance, and regulation to strike the appropriate balance. (shrink)

BackgroundEthics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.Main textIn this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map (...) these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.ConclusionsWe believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large. (shrink)

Mobile health research involving pervasive sensors, mobile apps and other novel data collection tools and methods present new ethical, legal, and social challenges specific to informed consent, data management and bystander rights. To address these challenges, a participatory design approach was deployed whereby stakeholders contributed to the development of a web-based commons to support the mHealth research community including researchers and ethics board members. The CORE platform now features a community forum, a resource library and a network of nearly 600 (...) global members. The utility of the participatory design process was evaluated by analyzing activities carried out over an 8-month design phase consisting of 86 distinct events including iterative design deliberations and social media engagement. This article describes how participatory design yielded 55 new features directly mapped to community needs and discusses relationships to user engagement as demonstrated by a steady increase in CORE member activity and followers on Twitter. (shrink)

Shen et al. (2024) have articulated the many opportunities as well as notable challenges associated with the emerging practice of returning individual research results (IRR) specific to digital phe...

The development and use of large and dynamic health data repositories designed to support research pose challenges to traditional informed consent models. We used semi-structured interviewing to elicit diverse research stakeholders' views of a model of consent appropriate to participation in initiatives that entail collection, long-term storage, and undetermined future research use of multiple types of health data. We demonstrate that, when considering health data repositories, research stakeholders replace a concept of consent as informed with one in which consent is (...) engaged. In engaged consent, a participant's ongoing relationship with a repository serves as a substitute or adjunct to information exchange at enrollment. We detail research stakeholders' views of the risks of engaged consent and suggest questions for further study about engagement and consent procedures in initiatives that aim to store data for future unspecified research purposes. (shrink)