Scientific data‐sharing and open science initiatives are increasingly important mechanisms for advancing the impact of genomic research. These mechanisms are being implemented as growing attention is paid to the need to improve the inclusion of research participants from marginalized and underrepresented groups. Together, these efforts aim to promote equitable advancements in genomic medicine. However, if not guided by community‐informed protections, these efforts may harm the very participants and communities they aim to benefit. This essay examines potential benefits and harms of (...) open science and explores how to advance a more just vision of open science in genomics. Drawing on relational ethics frameworks, we argue that researchers should consider their obligations to participants as well as the broader communities that are impacted by their research. We propose eight strategies to provide a foundation of practical steps for researchers to reduce the possibility of harms stemming from open science and to work toward genomic justice. (shrink)

Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood (...) to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. (shrink)

Digital phenotyping for mental health is an emerging trend which uses digital data, derived from mobile applications, wearable technologies and digital sensors, to measure, track and predict the mental health of an individual. Digital phenotyping for mental health is a growing, but as yet underexamined, field. As we will show, the rapid growth of digital phenotyping for mental health raises crucial questions about the values that underpin and are reinforced by this technology, as well as regarding to whom it may (...) become valuable. In this commentary, we explore these questions by focusing on the construction of value across two interrelated domains: user experience and epistemologies on the one hand, and issues of data and ownership on the other. In doing so, we demonstrate the need for a deeper ethical and epistemological engagement with the value assumptions that underpin the promise of digital phenotyping for mental health. (shrink)

Aims To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. Methods We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA. (...) Results We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. Conclusion Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology. (shrink)

Zusammenfassung Problemhintergrund Biomedizinische Forschung auf Grundlage von Big Data bietet immense Vorteile. Groß angelegte multizentrische Forschung, die große Mengen persönlicher Gesundheitsdaten einbezieht, v. a. genetische und genomische Daten, könnte zu einer stärker personalisierten Medizin beitragen. Dieser Typ Forschung erfordert den Transfer und die Speicherung hochsensibler Daten, wodurch sich die Frage ergibt, wie Datensubjekte vor „data harm“ geschützt werden können, etwa vor Verletzungen der Privatsphäre, Disempowerment, Verlust von Rechten und Ausbeutung. Hier ergibt sich das Dilemma, wie die Vorteile der auf Big (...) Data basierenden biomedizinischen Forschung zu nutzen sind und zugleich das Recht von Datensubjekten auf informationelle Selbstbestimmung zu schützen ist. Argumentation Blockchain-Technologien werden aufgrund ihrer Features häufig als technische Lösung des genannten Dilemmas diskutiert. Dazu gehören die Herkunftsbestimmung von Daten, Dezentralisierung, Unveränderbarkeit sowie Zugang und das Governance-System. Allerdings wirft die Implementierung von Blockchain-Technologien in der biomedizinischen Forschung auch Fragen auf, besonders hinsichtlich Consent, rechtlicher Rahmenbedingungen und Integration in den Workflow. Somit bedarf es begleitender Maßnahmen, die ich als „enabler“ bezeichne, um das Potenzial von Blockchain-Technologien aktualisieren zu können. Zu diesen gehören innovative Consent-Modelle, Modelle zum Datenbesitzrecht und regulatorische Modelle. Schlussfolgerung Blockchain-Technologien als bloß technische Lösung sind unzureichend hinsichtlich des beschriebenen Dilemmas. Die Kombination aus dieser technischen Lösung und den genannten „enablers“ könnte der richtige Weg sein, um auf Big Data fußende biomedizinische Forschung zu ermöglichen und zugleich die informationelle Selbstbestimmung von Datensubjekten zu schützen. (shrink)

BackgroundResearch using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The research-based health industry is also interested in so-called ‘real-world’ health data to develop new drugs, medical technologies or data-based health applications. While access to medical data is handled very differently in different countries, and some empirical data suggest people are uncomfortable with the idea of companies accessing health information, this paper (...) aims to advance the ethical debate about secondary use of medical data generated in the public healthcare sector by for-profit companies for medical research (ReuseForPro).MethodsWe first clarify some basic concepts and our ethical-normative approach, then discuss and ethically evaluate potential claims and interests of relevant stakeholders: patients as data subjects in the public healthcare system, for-profit companies, the public, and physicians and their healthcare institutions. Finally, we address the tensions between legitimate claims of different stakeholders in order to suggest conditions that might ensure ethically sound ReuseForPro.ResultsWe conclude that there are good reasons to grant for-profit companies access to medical data if they meet certain conditions: among others they need to respect patients’ informational rights and their actions need to be compatible with the public’s interest in health benefit from ReuseForPro. (shrink)

Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite research that integrates large amounts of personal health data, especially genomic and genetic data, might contribute to a more personalized medicine. This type of research requires the transfer and storage of highly sensitive data, which raises the question of how to protect data subjects against data harm, such as privacy breach, disempowerment, disenfranchisement, and exploitation. As a result, there is a trade-off between reaping the benefits (...) of big-data-based biomedical research and protecting data subjects’ right to informational privacy. Arguments Blockchain technologies are often discussed as a technical fix for the abovementioned trade-off due to their specific features, namely data provenance, decentralization, immutability, and access and governance system. However, implementing blockchain technologies in biomedical research also raises questions regarding consent, legal frameworks, and workflow integration. Hence, accompanying measures, which I call enablers, are necessary to unleash the potential of blockchain technologies. These enablers are innovative models of consent, data ownership models, and regulatory models. Conclusion Blockchain technologies as a technical fix alone is insufficient to resolve the aforementioned trade-off. Combining this technical fix with the enablers outlined above might be the best way to perform biomedical research based on big data and at the same time protect the informational privacy of data subjects. (shrink)

Public trust is paramount for the well functioning of data driven healthcare activities such as digital health interventions, contact tracing or the build-up of electronic health records. As the use of personal data is the common denominator for these healthcare activities, healthcare actors have an interest to ensure privacy and anonymity of the personal data they depend on. Maintaining privacy and anonymity of personal data contribute to the trustworthiness of these healthcare activities and are associated with the public willingness to (...) trust these activities with their personal data. An analysis of online news readership comments about the failed care.data programme in England revealed that parts of the public have a false understanding of anonymity in the context of privacy protection of personal data as used for healthcare management and medical research. Some of those commenting demanded complete anonymity of their data to be willing to trust the process of data collection and analysis. As this demand is impossible to fulfil and trust is built on a false understanding of anonymity, the inability to meet this demand risks undermining public trust. Since public concerns about anonymity and privacy of personal data appear to be increasing, a large-scale information campaign about the limits and possibilities of anonymity with respect to the various uses of personal health data is urgently needed to help the public to make better informed choices about providing personal data. (shrink)

The idea of a ‘right to mental integrity’, sometimes referred to as a ‘right against mental interference,’ is a relatively new concept in bioethics, making its way into debates about neurotechnological advances and the establishment of ‘neurorights.’ In this paper, we interrogate the idea of a right to mental integrity. First, we argue that some experts define the right to mental integrity so broadly that rights violations become ubiquitous, thereby trivialising some of the very harms the concept is meant to (...) address. Second, rights-based framing results in an overemphasis on the normative importance of consent, implying that neurointerventions are permissible in cases where people consent to have their mental states influenced or read off, a confidence in consent that we argue is misguided. Third, the concept often collapses the ethics of brain inputs and brain outputs, potentially resulting in a loss of important conceptual nuance. Finally, we argue that the concept of a right to mental integrity is superfluous—what is wrong with most violations of mental integrity can be explained by existing concepts such as autonomy, manipulation, privacy, bodily rights, surveillance, harm and exploitation of vulnerabilities. We conclude that bioethicists and policy-makers ought to either make use of these concepts rather than arguing for the existence of a new right, or they need to avoid making rights violations ubiquitous by settling on a narrower and more rigorous definition of the right. (shrink)

A learning health system (LHS) seeks to establish a closer connection between clinical care and research and establishes new responsibilities for healthcare providers as well as patients. A new set of technological approaches in medication adherence monitoring can potentially yield valuable data within an LHS, and raises the question of the scope and limitations of patients’ responsibilities to use them. We argue here that, in principle, it is plausible to suggest that patients have a prima facie obligation to use novel (...) adherence monitors. However, the strength of the obligations depends considerably on the extent to which data that adherence monitors generate are, in fact, used to further the goals of LHSs. The way in which data ownership is structured in the USA poses a considerable challenge here, while the European Union framework offers a more promising alternative. (shrink)

Responsible Research and Innovation (‘RRI’) is a cross-cutting priority for scientific research in the European Union and beyond. This paper considers whether the way such research is organised and delivered lends itself to the aims of RRI. We focus particularly on international consortia, which have emerged as a common model to organise large-scale, multi-disciplinary research in contemporary biomedical science. Typically, these consortia operate through fixed-term contracts, and employ governance frameworks consisting of reasonably standard, modular components such as management committees, advisory (...) boards, and data access committees, to co-ordinate the activities of partner institutions and align them with funding agency priorities. These have advantages for organisation and management of the research, but can actively inhibit researchers seeking to implement RRI activities. Conventional consortia governance structures pose specific problems for meaningful public and participant involvement, data sharing, transparency, and ‘legacy’ planning to deal with societal commitments that persist beyond the duration of the original project. In particular, the ‘upstream’ negotiation of contractual terms between funders and the institutions employing researchers can undermine the ability for those researchers to subsequently make decisions about data, or participant remuneration, or indeed what happens to consortia outputs after the project is finished, and can inhibit attempts to make project activities and goals responsive to input from ongoing dialogue with various stakeholders. Having explored these challenges, we make some recommendations for alternative consortia governance structures to better support RRI in future. (shrink)

Medical artificial intelligence (AI) is considered to be one of the most important assets for the future of innovative individual and public health care. To develop innovative medical AI, it is necessary to repurpose data that are primarily generated in and for the health care context. Usually, health data can only be put to a secondary use if data subjects provide their informed consent (IC). This regulation, however, is believed to slow down or even prevent vital medical research, including AI (...) development. For this reason, a number of scholars advocate a moral civic duty to share electronic health records (EHRs) that overrides IC requirements in certain contexts. In the medical AI context, the common arguments for such a duty have not been subjected to a comprehensive challenge. This article sheds light on the correlation between two normative discourses concerning informed consent for secondary health record use and the development and use of medical AI. There are three main arguments in favour of a civic duty to support certain developments in medical AI by sharing EHRs: the ‘rule to rescue argument’, the ‘low risks, high benefits argument’, and the ‘property rights argument’. This article critiques all three arguments because they either derive a civic duty from premises that do not apply to the medical AI context, or they rely on inappropriate analogies, or they ignore significant risks entailed by the EHR sharing process and the use of medical AI. Given this result, the article proposes an alternative civic responsibility approach that can attribute different responsibilities to different social groups and individuals and that can contextualise those responsibilities for the purpose of medical AI development. (shrink)