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Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy-in turn raising an important problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network's ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue., Journal of Business Ethics, ISSN:0167-4544, ISSN:1573-0697

This paper describes the results of a multi-country survey of governance approaches for the use of digital contact tracing (DCT) in response to the COVID-19 pandemic. We argue that the countries in our survey represent two distinct models of DCT governance, both of which are flawed. The “data protection model” emphasizes privacy protections at the expense of public health benefit, while the “emergency response model” sacrifices transparency and accountability, prompting concerns about excessive governance surveillance. The ethical and effective use of DCT in the future requires a new governance approach that is better suited to this novel use of mobile phone data to promote public health.”

Anticipating the diagnosis of Alzheimer's Disease (AD) and other dementias depends on seeing such conditions as progressive neuropathological phenomena originating before the clinical manifestation...

The expression “public opinion” has long been part of common parlance. However, its value as a scientific measure has been the topic of abundant academic debates over the past several decades. Such debates have produced more variety and contestations rather than consensus on the very definition of public opinion, let alone on how to measure it. This study reports on the usefulness of web-based big data digital network analytics in deciphering the distributed meanings and sense making related to controversial biotechnology applications. Using stem cell therapies as a case study, we argue that such digital network analysis can complement the traditional opinion polls while avoiding the sampling bias that is typical of opinion polls. Although the polls cannot account for the opinion dynamics, combining them with web-based big data analysis can shed light on three dimensions of public opinion essential for sense making: counts or volume of opinion data, content, and movement of opinions. This approach is particularly promising in the case of ongoing scientific controversies that increasingly overflow into the public sphere morphing into public political debates. In particular, our study focuses as a case study on public controversies over the clinical provision of stem cell therapies. Using web entities specifically addressing stem cell issues, including their dynamic aggregation, the internal architecture of the web corpus we report in this study brings the third dimension of public opinion (movement) into sharper focus. Notably, the corpus of stem cell networks through web connectivity presents hot spots of distributed meaning. Large-scale surveys conducted on these issues, such as the Eurobarometer of Biotechnology, reveal that European citizens only accept research on stem cells if they are highly regulated, while the stem cell digital network analysis presented in this study suggests that distributed meaning is promise centeredness. Although major scientific journals and companies tend to structure public opinion networks, our finding of promise centeredness as a key ingredient of distributed meaning and sense making is consistent with therapeutic tourism that remains as an important facet of the stem cell community despite the lack of material standards. This new approach to digital network analysis has crosscutting corollaries for rethinking the notion of public opinion, be it in electoral preferences or as we discuss in this study, for new ways to measure, monitor, and democratically govern emerging technologies.

Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner., PLOS Digital Health, 2 (1), ISSN:2767-3170

This article examines biobank transparency mechanisms vis-à-vis their public information, as found on the public biobank webpages. Two independent studies about biobank governance in Europe and Canada identified a lack of governance-related information provided by biobanks on their public webpages. This lack of transparency stands in contrast to governance best practice guidelines highlighting the importance of transparency as a principle of good governance. Transparency is especially important as many biobanks are publicly funded, and it contributes to accountability and supports the development of donor trust in biobanks. Empirical evidence shows that the public supports greater transparency about biobank governance. It will be important that information provided online is relevant and accessible for a variety of different stakeholders (e.g. public and private sector scientists and institutions, donors and potential donors, members of the public). Transparency standards, however, need to be proportionate to avoid the situation that only large-scale biobanks can allocate appropriate resources to fulfil them. Implementing adequate standards of transparency about biobanks' governance will increase accountability but also allow current and future participants to make more informed decisions about their participation in biobank activities.

On June 7, 2021, the US Food and Drug Administration (FDA) approved aducanumab, a monoclonal amyloid targeting β-amyloid, for the treatment for Alzheimer's disease (AD). This decision was achieved through the Accelerated Approval Pathway and was essentially motivated by the evidence that aducanumab reduces brain amyloid plaques. This news is causing a heated debate in the scientific community. On the one hand, aducanumab is the first drug to be approved for the treatment of the disease since 2003 and is the first drug to act on the alleged pathophysiological mechanisms of AD. At the same time, the evidence of clinical benefit coming from two phase 3 clinical trials is contradictory and still inconclusive. The aim of the present editorial is to provide some points to consider that can help understand the peculiarities and implications of this approval and feed the scientific debate underway.

The management of frailty in older persons is not easy, implying interventions beyond the simple prescription of medications. Biological complexity, multimorbidity, polypharmacy, and social issues often hamper the possibility to directly translate the evidence coming from research into clinical practice. Frailty indeed represents the most relevant cause of the “evidence-based medicine issue” influencing clinical decisions in geriatric care. Today, patients with Alzheimer’s disease (AD) are much older and frailer than some decades ago. They also tend to have more drugs prescribed. In parallel, research on AD has evolved over the years, hypothesizing that anticipating the interventions to the earliest stages of the disease may provide beneficial effects (to date, still lacking). In this article, we argue that, by focusing exclusively on “the disease” and pushing to anticipate its detection (sometimes even before the appareance of its clinical manifestations) may overshadow the person’s values and priorities. Research should be developed for better integrating the concept of aging and frailty in the design of clinical trials in order to provide results that can be implemented in real life. On the other hand, clinicians should be less prone to the easy (but unsupported by evidence) pharmacological prescription.

This chapter explores ethical issues raised by the use of artificial intelligence (AI) in the domain of biomedical research, healthcare provision, and public health. The litany of ethical challenges that AI in medicine raises cannot be addressed sufficiently by current regulatory and ethical frameworks. The chapter then advances the systemic oversight approach as a governance blueprint, which is based on six principles offering guidance as to the desirable features of oversight structures and processes in the domain of data-intense biomedicine: adaptivity, flexibility, inclusiveness, reflexivity, responsiveness, and monitoring (AFIRRM). In the research domain, ethical review committees will have to incorporate reflexive assessment of the scientific and social merits of AI-driven research and, as a consequence, will have to open their ranks to new professional figures such as social scientists. In the domain of patient care, clinical validation is a crucial issue. Hospitals could equip themselves with “clinical AI oversight bodies” charged with the task of advising clinical administrators. Meanwhile, in the public health sphere, the new level of granularity enabled by AI in disease surveillance or health promotion will have to be negotiated at the level of targeted communities.

With the birth of genetically engineered twins in November 2018, international debate about human genome editing governance has moved from an emphasis on mutual engagement among multiple stakeholders to a self-regulatory model enacted through high-level expert groups with little or no public input. This article reconstructs this paradigm shift and suggests that inclusive public deliberation should still have a role in public decision making about genome editing.

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features (adaptivity, flexibility, monitoring, responsiveness, reflexivity, and inclusiveness) and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact unless oversight activities by different stakeholders acquire a common substantive orientation.

The online space has become a digital public square, where individuals interact and share ideas on the most trivial to the most serious of matters, including discussions of controversial ethical issues in science, technology and medicine. In the last decade, new disciplines like computational social science and social data science have created methods to collect and analyse such data that have considerably expanded the scope of social science research. Empirical bioethics can benefit from the integration of such digital methods to investigate novel digital phenomena and trace how bioethical issues take shape online.Here, using concrete examples, we demonstrate how novel methods based on digital approaches in the social sciences can be used effectively in the domain of bioethics. We show that a digital turn in bioethics research aligns with the established aims of empirical bioethics, integrating with normative analysis and expanding the scope of the discipline, thus offering ways to reinforce the capacity of bioethics to tackle the increasing complexity of present-day ethical issues in science and technology. We propose to call this domain of research in bioethicsdigital bioethics.

"Since the first successful application of the gene editing method based on the CRISPR/Cas-system, the technology has demonstrated great potential but also sparked a series of ethical concerns. Some of the issues are already known from earlier gene editing debates. However, the possibility of CRISPR to target genes with high accuracy and the easy application that allows a biohacker to experiment with a simple toolkit ordered online have introduced new ethical challenges. Further, thanks to preprint servers such as bioRxiv, biomedical research results are more and more accessible with little delay after an experiment was conducted. This enables researchers all over the world to participate and conduct their own experiments, making it a global endeavour. Not only does this make it difficult to monitor and regulate the technology but also speeds up the technological development significantly.CRISPR is only one of many examples of recent advancements with potentially high consequences for society at large. We think it is therefore paramount to identify new issues, understand their nature and assess their impact in a timely manner. In this paper, we propose the integration of digital methods into the toolbox of modern empirical bioethics and demonstrate their potential with two examples: We used 1) crawling and network analysis for hypothesis building, and 2) sentiment analysis to assess the public’s attitudes towards CRISPR on Twitter over a six and a half years period. "

In this paper, we discuss how access to health-related data by private insurers, other than affecting the interests of prospective policy-holders, can also influence their propensity to make personal data available for research purposes. We take the case of national precision medicine initiatives as an illustrative example of this possible tendency. Precision medicine pools together unprecedented amounts of genetic as well as phenotypic data. The possibility that private insurers could claim access to such rapidly accumulating biomedical Big Data or to health-related information derived from it would discourage people from enrolling in precision medicine studies. Should that be the case, the economic value of personal data for the insurance industry would end up affecting the public value of data as a scientific resource. In what follows we articulate three principles – trustworthiness, openness and evidence – to address this problem and tame its potentially harmful effects on the development of precision medicine and, more generally, on the advancement of medical science. ispartof: Big Data & Society vol:6 issue:1 pages:1-6 status: Published online

This study reports on 13 semistructured in-depth interviews to qualitatively explore the experiences of individuals who publicly shared their direct-to-consumer genetic testing results on the platform openSNP. In particular, we focused on interviewees' understanding of privacy. Participants reported that the likelihood and the magnitude of privacy harms depend on gender, ethnicity, sexual orientation, the stigma associated with certain clinical conditions, the existence of adequate legislation, and the nature of national health care systems. Some participants expressed the view that those who enjoy higher socioeconomic status or are better protected by their country's legislation have a responsibility to share their genetic data. Our study shows that people who share their genetic data publicly online-far from being insensitive to privacy risks-have a complex understanding of the social, relational, and contextual nature of genetic privacy.

This chapter focuses on ethical issues raised by the use of artificial intelligence (AI) in the do-main of health. In particular we discuss specific issues in biomedical research, healthcare provision and public health. We devote particular attention to ethical concerns about safety and evidence standards in biomedical AI; to informed consent and the impact of automation on both professional caregivers and patients; to fairness and discrimination in algorithmic decision making on treatment and disease prevention for individuals and populations; to equity and social justice in the distribution of AI-driven health care and public health. We argue that the litany of ethical challenges that AI in medicine raises cannot be addressed sufficiently by current regulatory and ethical frameworks. We thus propose relevant governance approaches that can help address this gap.

Much has been written about the ethics of healthcare innovation and the role of pharmaceutical companies in innovation. Rightly or wrongly the scholarship and media coverage has been disparaging. Many commentators fear that the biopharmaceutical sector no longer serves patients’ interests; they see it as more focused on shareholders’ nancial interests.

BACKGROUND Information and communication technologies have long become prominent components of health systems. Rapid advances in digital technologies and data science over the last few years are predicted to have a vast impact on health care services, configuring a paradigm shift into what is now commonly referred to as digital health. Forecasted to curb rising health costs as well as to improve health system efficiency and safety, digital health success heavily relies on trust from professional end users, administrators, and patients. Yet, what counts as the building blocks of trust in digital health systems has so far remained underexplored. OBJECTIVE The objective of this study was to analyze what relevant stakeholders consider as enablers and impediments of trust in digital health. METHODS We performed a scoping review to map out trust in digital health. To identify relevant digital health studies, we searched 5 electronic databases. Using keywords and Medical Subject Headings, we targeted all relevant studies and set no boundaries for publication year to allow a broad range of studies to be identified. The studies were screened by 2 reviewers after which a predefined data extraction strategy was employed and relevant themes documented. RESULTS Overall, 278 qualitative, quantitative, mixed-methods, and intervention studies in English, published between 1998 and 2017 and conducted in 40 countries were included in this review. Patients and health care professionals were the two most prominent stakeholders of trust in digital health; a third—health administrators—was substantially less prominent. Our analysis identified cross-cutting personal, institutional, and technological elements of trust that broadly cluster into 16 enablers (altruism, fair data access, ease of use, self-efficacy, sociodemographic factors, recommendation by other users, usefulness, customizable design features, interoperability, privacy, initial face-to-face contact, guidelines for standardized use, stakeholder engagement, improved communication, decreased workloads, and service provider reputation) and 10 impediments (excessive costs, limited accessibility, sociodemographic factors, fear of data exploitation, insufficient training, defective technology, poor information quality, inadequate publicity, time-consuming, and service provider reputation) to trust in digital health. CONCLUSIONS Trust in digital health technologies and services depends on the interplay of a complex set of enablers and impediments. This study is a contribution to ongoing efforts to understand what determines trust in digital health according to different stakeholders. Therefore, it offers valuable points of reference for the implementation of innovative digital health services. Building on insights from this study, actionable metrics can be developed to assess the trustworthiness of digital technologies in health care.

Digital health encompasses a wide range of novel digital technologies related to health and medicine. Such technologies rely on recent advances in the collection and analysis of ever increasing amounts of data from both patients and healthy citizens. Along with new opportunities, however, come new ethical and policy challenges. These range from the need to adapt current evidence-based standards, to issues of privacy, oversight, accountability and public trust as well as national and international data governance and management. This review illustrates key issues and challenges facing the rapidly unfolding digital health paradigm and reflects on the impact of big data in medical research and clinical practice both internationally and in Switzerland. It concludes by emphasising five conditions that will be crucial to fulfil in order to foster innovation and fair benefit sharing in digital health., Swiss Medical Weekly, 148, ISSN:1424-7860, ISSN:1424-3997

Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy., Frontiers in Medicine, 5, ISSN:2296-858X

This opionion paper is published on Elephant in the lab – a blog journal on science policy. It is part of a series of articles analysing the meaning and future of scientific impact. Follow the blog: http://www.elephantinthelab.org/ – Twitter: @EITL_blog

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Europe is ready to deploy its immense capital of knowledge into the development of effective cell-based therapies and delve into the global race for translating stem cell science into regenerative medicine. But what are the challenges and the emerging issues that lay ahead the realization of Europe's enormous potential in this field? Both researchers and industrial stakeholders tend to impute the slow pace of translation to specific suboptimal features of the regulatory environment in Europe. At the same time, a host of new issues are emerging as testified by a recent public controversy regarding the provision of unproven cell therapy in Italy. We will review this topic and suggest some solutions to foster the responsible development of innovative cell-based therapies in Europe.

Dans cet article, l’auteur presentera deux corpus de lignes directrices professionnelles etablies par deux societes savantes œuvrant dans le domaine de la medecine regenerative utilisant des cellules souches : la « Societe internationale pour la recherche sur les cellules souches » (ISSCR) et la « Societe internationale de medecine cellulaire » (ICMS). Ces lignes directrices presentent des points de vue divergents sur la facon dont les cellules souches devraient etre utilisees en therapie pour les patients. Elles demontrent l’existence de postures alternatives pour l’encadrement de l’utilisation clinique de ces cellules en s’appuyant sur des hypotheses ontologiques, epistemologiques, ethiques et reglementaires differentes. Ces deux interpretations radicalement opposees sur la facon dont les cellules souches devraient etre transposees pour des applications cliniques, s’ancrent respectivement sur des postures narratives soit de normalisation soit de naturalisation. Ces dernieres sont envisagees comme des ressources pragmatiques- discursives permettant d’articuler le present et l’avenir de la medecine regenerative. S’interesser aux differentes voies de normalisation de la medecine regenerative constitue une veritable opportunite pour une comprehension approfondie de la constitution meme des options du passage a l’utilisation clinique des cellules souches. En effet, dans le futur, la medecine regenerative va continuer a evoluer puisque de nouvelles connaissances scientifiques s’accumuleront et de nouvelles pratiques cliniques seront experimentees. On peut meme soutenir que la controverse sera plus importante dans la mesure ou les acteurs essaient de construire la cohesion professionnelle et de nourrir la confiance du public dans leurs activites scientifiques. En consequence, suivre la trajectoire de ces modeles translationnels tels qu’ils sont proposes, contestes, rejetes ou soutenus par les acteurs scientifiques et les autorites reglementaires sera fondamental pour permettre une evaluation critique des regimes d’encadrement qui stabiliseront ce domaine emergent de l’innovation scientifique.

L'articolo prende in esame i problemi etici connessi con le scienze della vita e i modelli di decisione pubblica messi in atto per fare fronte al crescente disaccordo morale sui metodi e gli scopi della ricerca. Mostra inoltre come negli ultimi decenni si siano affermati modelli didella scienza improntati a visioni differenti dei rapporti tra comunitŕ scientifica, comunitŕ bioetica e decisori pubblici. Fornisce infine un'analisi dei fondamenti teorici generali di tali modelli.

Sentience is a capacity that only some organisms have. It is often assumed that this capacity has moral significance and should have a prominent role in moral thinking. The commonsense notion of sentience (as indicated by the etymology, from the Latin sentiens which means “capable of feeling”) suggests that sentience is a capacity to be in certain kinds of mental states. In the ethical literature, the term “sentience” is often used to refer to the ability to feel pain and pleasure. More generally, sentience can be said to be the capacity to have feelings or sensations, which include pain and pleasure but also visual and auditory sensations, hunger, sadness, and so on. Keywords: abortion; agency; Bentham, Jeremy; consequentialism; ecology; intentionality; moral status; practical (applied) ethics; rights; Singer, Peter

The history of stem cell research is considered a recent one, yet, it began as early as the middle of the nineteenth century and is rich in questions, approaches, concepts, tools, methodologies and lines of thought. There are at least nine disciplines or research fields which have made major contributions: botany and horticulture; cell theory; evolutionary biology; embryology and developmental biology; haematology; cell and tissue culture; regeneration biology; teratology and teratogeny and cancer research. Moreover, as soon as efforts aimed at extracting and culturing pluripotent cells from human embryos proved successful in 1998, stem cells became objects of bioethical controversy. The moral status of the embryo will be discussed, together with proposed technical means to avoid its destruction, such as altered nuclear transfer, single blastomere biopsy, cytoplasmic hybrids and induced pluripotent stem cells, and the possible ethical problems associated with future medical uses of stem cells. Key Concepts: Far from being a simple technology or a commodity, the study of stem cells concerns a broad spectrum of issues lying at the heart of the phenomenon of life itself, and thus touches on questions that have been on the agenda of biological inquiry for a long time. Like most biological research fields, the history of stem cell research shows a constant weaving and unweaving of many concepts and tools, coming from different experimental settings, lines of thought and at least nine different biomedical areas. At some moments of its history, the ‘stem cell’ has become a more concrete and even manageable object, at other moments it was found difficult to localise the phenomenon of self-renewal in one or in a few cells. Recently, stem cell research is characterised by harsh bioethical controversy, mainly revolving around the issue of the moral status of human embryos. Scientists have proposed some technical ways for circumventing ethical problems, but other morally relevant issues still lie before stem cell research. In the future, medical innovation through stem cell-based regenerative medicine is likely to give rise to issues regarding safety, ethical oversight and distributive justice. Keywords: regeneration; embryo; bioethics; altered nuclear transfer (ANT); induced pluripotent stem (iPS) cell; cybrid; distributive justice

Who among the many researchers is most likely to usher in a new era of scientific breakthroughs? This question is of critical importance to universities, funding agencies, as well as scientists who must compete under great pressure for limited amounts of research money. Citations are the current primary means of evaluating one’s scientific productivity and impact, and while often helpful, there is growing concern over the use of excessive self-citations to help build sustainable careers in science. Incorporating superfluous self-citations in one’s writings requires little effort, receives virtually no penalty, and can boost, albeit artificially, scholarly impact and visibility, which are both necessary for moving up the academic ladder. Such behavior is likely to increase, given the recent explosive rise in popularity of web-based citation analysis tools (Web of Science, Google Scholar, Scopus, and Altmetric) that rank research performance. Here, we argue for new metrics centered on transparency to help curb this form of self-promotion that, if left unchecked, can have a negative impact on the scientific workforce, the way that we publish new knowledge, and ultimately the course of scientific advance., Publications, 5 (3), ISSN:2304-6775

Familial Alzheimer's disease (FAD), despite representing a rare condition, is attracting a growing interest in the scientific community. Improved phenotyping of FAD cases may have a relevant impact both in clinical and research contexts. We performed a systematic review of studies describing the phenotypic features of FAD cases sustained by PSEN2 mutations, the less common cause of monogenic AD. Special attention was given to the clinical manifestations as well as to the main findings coming from the most commonly and widely adopted diagnostic procedures. Basing on the collected data, we also attempted to conduct a genotype-phenotype correlation analysis. Overall, the mutations involving the PSEN2 gene represent an extremely rare cause of FAD, having been reported to date in less than 200 cases. They are mainly associated, despite some peculiar and heterogeneous features, to a typical AD phenotype. Nevertheless, the frequent occurrence of psychotic symptoms may represent a potential distinctive element. The scarcity of available phenotypic descriptions strongly limits the implementation of genotype-phenotype correlations.

Present adress of Alessandro Blasimme: INSERM UMR 1027, Université de Toulouse 3-Paul Sabatier; International audience; In this paper we discuss the conditions for the possession of intentional states (especially beliefs) and for intentional agency. We then explore the implications of an analysis of intentionality in non-human animals for their entitlement to ethical treat- ment, and review the potential advantages and epistemological difficulties of relying on the scientific study of animal mindedness to draw ethical conclusions. In the end, we argue that ethical debates on the treatment of animals, and in particular considera- tions about welfare, can benefit considerably from the enterprise of exploring the ex- tent to which non-human animals are minded.