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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.”

Dementia has lately undergone a profound reconceptualization. Long conceived of as an unpreventable process of mental deterioration, current evidence shows that it can be prevented in at least one in three cases intervening on a specified set of factors. Issues of justice and equity loom large on the implementation of dementia prevention, from a global health perspective. Our project thus embraces emerging evidence about dementia risk factors and their uneven distribution nationally and globally by specifically focusing on the situated aspects of dementia prevention.The aim of the BEAD study (Optimizing the Aging Brain? Situating Ethical Aspects in Dementia Prevention) is to dissect the ethical and clinical assumptions of this novel understanding of dementia, and to analyze how such new discourse on dementia prevention plays out in three countries: Canada, Germany and Switzerland.This study adopts a multi-perspective, comparative, qualitative approach, combining stakeholder interviews with different kinds of focused ethnographies, elaborating on conceptual, ethical, and social aspects of what we would like to call the "new dementia".By situating the paradigmatic shifts in Alzheimer's and dementia research within current aging cultures and contemporary social policies, we aim to initiate a debate about the often implicit unresolved social, ethical, and political implications and preconditions of the medical understanding and handling of cognitive disorders., The Journal of Frailty & Aging, 12 (2), ISSN:2260-1341, ISSN:2273-4309

Over the last decade, bioengineering has seen a sustained growth in scientific publications, patents, and clinical trials. As the field attempts to bridge the gap between discovery and clinical application, a broader societal dialog is needed to build public trust and address potential ethical, societal, and regulatory challenges. In this essay, we discuss societal aspects linked to the clinical use of biomedical engineering approaches and technologies, with a specific focus on molecular systems engineering. Drawing on data from interviews with 24 scientists, we identified four key aspects for fostering societal support for translational efforts in this domain: (1) effective science communication and internal awareness; (2) open societal dialogue; (3) fair and equitable access to new technologies; and (4) adequate science and technology policies. We conclude that molecular systems engineering would benefit from anticipating future challenges with the view of building a robust bond of trust with lay publics, regulators, and society at large., Bioengineering & Translational Medicine, ISSN:2380-6761

Introduction Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. Methods We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care., PLoS ONE, 18 (1), ISSN:1932-6203

Spurred by recent advances in machine learning and electronic hardware, digital health promises to profoundly transform medicine. At the same time, however, it raises conspicuous ethical and regulatory issues. This has led to a growing number of calls for responsible digital health. Based on stakeholder engagement methods, this paper sets out to identify core impediments hindering responsible digital health in Switzerland. We developed a participatory research methodology to access stakeholders' fragmented knowledge of digital health, engaging 46 digital health stakeholders over a period of five months (December 2020–April 2021). We identified ineffective stakeholder collaboration, lack of ethical awareness among digital health innovators, and lack of relevant regulation as core impediments to responsible digital health. The stakeholders' accounts indicate that ethical concerns may considerably slow the pace of digital health innovation – implying that responsible innovation is a core catalyst for the progress of digital health overall., Frontiers in Digital Health, 5, ISSN:2673-253X

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...

Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.

Good biobank governance implies—at a minimum—transparency and accountability and the implementation of oversight mechanisms. While the biobanking community is in general committed to such principles, little is known about precisely which governance strategies biobanks adopt to meet those objectives. We conducted an exploratory analysis of governance mechanisms adopted by research biobanks, including genetic biobanks, located in Europe and Canada. We reviewed information available on the websites of 69 biobanks, and directly contacted them for additional information. Our study identified six types of commonly adopted governance strategies: communication, compliance, expert advice, external review, internal procedures, and partnerships. Each strategy is implemented through different mechanisms including, independent ethics assessment, informed consent processes, quality management, data access control, legal compliance, standard operating procedures and external certification. Such mechanisms rely on a wide range of bodies, committees and actors from both within and outside the biobanks themselves. We found that most biobanks aim to be transparent about their governance mechanisms, but could do more to provide more complete and detailed information about them. In particular, the retrievable information, while showing efforts to ensure biobanks operate in a legitimate way, does not specify in sufficient detail how governance mechanisms support accountability, nor how they ensure oversight of research operations. This state of affairs can potentially undermine biobanks’ trustworthiness to stakeholders and the public in a long-term perspective. Given the ever-increasing reliance of biomedical research on large biological repositories and their associated databases, we recommend that biobanks increase their efforts to future-proof their governance., European Journal of Human Genetics, 28, ISSN:1476-5438, ISSN:1018-4813

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

Aim To describe the methods and feasibility of the nutritional intervention carried out within the SPRINTT Randomized cotrolled trial. We also illustrate how nutrition interventionists identified participants at risk of malnutrition and the lessons learnt from the nutrition intervention. Findings SPRINTT nutrition intervention was well-received by the majority of the participants. It was mainly carried out using tailored nutrition counselling, but also other means of delivering the intervention were successfully used. Compared with a standard nutrition prescription, an individualized protocol to diagnose malnutrition and follow-up by tailored nutrition counselling helped achieve nutritional targets more effectively in spite of diversity of population in nutritional habits and in some cases reluctance to accept changes. Message The SPRINTT nutrition intervention was feasible and allowed flexibility to the varying needs and cultural differences of this heterogeneous population of frail, older Europeans. It may serve as a model to educate and improve nutrition among community-dwelling older people at risk of mobility limitations. Supplementary Information The online version contains supplementary material available at 10.1007/s41999-020-00438-4., Background The “Sarcopenia and Physical Frailty in Older People: Multicomponent Treatment Strategies” (SPRINTT) project sponsored a multi-center randomized controlled trial (RCT) with the objective to determine the effect of physical activity and nutrition intervention for prevention of mobility disability in community-dwelling frail older Europeans. We describe here the design and feasibility of the SPRINTT nutrition intervention, including techniques used by nutrition interventionists to identify those at risk of malnutrition and to carry out the nutrition intervention. Methods SPRINTT RCT recruited older adults (≥ 70 years) from 11 European countries. Eligible participants (n = 1517) had functional limitations measured with Short Physical Performance Battery (SPPB score 3–9) and low muscle mass as determined by DXA scans, but were able to walk 400 m without assistance within 15 min. Participants were followed up for up to 3 years. The nutrition intervention was carried out mainly by individual nutrition counseling. Nutrition goals included achieving a daily protein intake of 1.0–1.2 g/kg body weight, energy intake of 25–30 kcal/kg of body weight/day, and serum vitamin D concentration ≥ 75 mmol/L. Survey on the method strategies and feasibility of the nutrition intervention was sent to all nutrition interventionists of the 16 SPRINTT study sites. Results Nutrition interventionists from all study sites responded to the survey. All responders found that the SPRINTT nutrition intervention was feasible for the target population, and it was well received by the majority. The identification of participants at nutritional risk was accomplished by combining information from interviews, questionnaires, clinical and laboratory data. Although the nutrition intervention was mainly carried out using individual nutritional counselling, other assisting methods were used as appropriate. Conclusion The SPRINTT nutrition intervention was feasible and able to adapt flexibly to varying needs of this heterogeneous population. The procedures adopted to identify older adults at risk of malnutrition and to design the appropriate intervention may serve as a model to deliver nutrition intervention for community-dwelling older people with mobility limitations. Supplementary Information The online version contains supplementary material available at 10.1007/s41999-020-00438-4.

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 SARS-CoV-2 pandemic is a public health challenge of unprecedented scale. In the midst of the first wave of the pandemic, governments worldwide introduced digital contact tracing systems as part of a strategy to contain the spread of the virus. In Europe, after intense discussion about privacy-related risks involving policymakers, technology experts, information technology companies, and—albeit to a limited extent—the public at large, technical protocols were created to support the development of privacy-compatible proximity tracing apps. However, as the second wave of SARS-CoV-2 sweeps the continent, digital contact tracing in Europe is evolving in terms of both technological and governance features. To enable policymakers to harness the full potential of digital health tools against SARS-CoV-2, this paper examines the evolution of digital contact tracing in eight European countries. Our study highlights that while privacy and data protection are at the core of contact tracing apps in Europe, countries differ in their technical protocols, and in their capacity to utilize collected data beyond proximity tracing alone. In particular, the most recently released apps tend to offer users more granular information about risk in specific locations, and to collect data about user whereabouts, in order to enhance retrospective contact tracing capacity. These developments signal a shift from a strict interpretation of data minimization and purpose limitation toward a more expansive approach to digital contact tracing in Europe, calling for careful scrutiny and appropriate oversight., Frontiers in Digital Health, 3, ISSN:2673-253X

In recent times, biomedical research has begun to tap into larger-than-ever collections of different data types. As a consequence, the notion of health data – data that are of relevance for health-related research or for clinical purposes – is expanding to include a variety of non-clinical data, as well as data provided by research participants themselves through commercially available products such as smartphones and fitness bands. To date, most scholarship and policy on these issues has focused on privacy and data protection. Less attention has been paid to addressing other aspects of the wicked challenges posed by big data health research and even less work has been geared towards the development of novel governance frameworks. In this chapter, we make the case for adaptive and principle-based governance of big data research. We outline six principles of adaptive governance for big data research and propose key factors for their implementation into effective governance structures and processes., Cambridge Law Handbooks, The Cambridge Handbook of Health Research Regulation, ISBN:978-1-108-47597-6, ISBN:978-1-108-62002-4

Whilst basic science rapidly produces new insights into the biological determinants of human health and disease, clinical innovation is often said to lag behind, as it fails to rapidly turn such knowledge into new tools for innovative patient care. This view of biomedical innovation constitutes a ‘translational lag narrative’, which is widely present in current research policy. This paper presents a qualitative content analysis of a corpus of documents (n = 28) issued by key policy actors in the domain of clinical translation between 2000 and 2018 in the United States and the European Union. The aim is to reconstruct how policy discourse articulates the translational lag narrative, and to analyze how the latter relates to specific sociotechnical imaginaries of progress and of the role of policymaking in their realization. The article identifies key impediments to clinical translation and highlights salient differences in the sociotechnical imaginaries of translation in the US and the EU. In the US, policy discourse around translation is mostly driven by the perceived need to re-instate linearity in the transition from knowledge-production to clinical innovation. In the European context, instead, the driving imaginary of the policy discourse around clinical translation has to do with constructing a distinctly European model of economic growth centered around the idea of a knowledge-based economy, thereby connecting policy stimuli for translation with broader political imaginations. This analysis elucidates how publicly staged narratives about science and technology in the biomedical field simultaneously contain state-specific visions of progress and statecraft., Humanities and Social Sciences Communications, 8 (1), ISSN:2662-9992

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.

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.

AJOB Neuroscience, 12 (4)

Background Ethics 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 text In 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. Conclusions We 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., BMC Medical Ethics, 22 (1), ISSN:1472-6939

In this paper, I present an emerging explanatory framework about ageing and care. In particular, I focus on how, in contrast to most classical accounts of ageing, biomedicine today construes the ageing process as a modifiable trajectory. This framing turns ageing from a stage of inexorable decline into the focus of preventive strategies, harnessing the functional plasticity of the ageing organism. I illustrate this shift by focusing on studies of the demographic dynamics in human population, observations of ageing as an intraspecifically heterogenous phenotype, and the experimental manipulation of longevity, in both model organisms and humans. I suggest that such an explanatory framework about ageing creates the epistemological conditions for the rise of a peculiar form of prevention that does not aim to address a specific condition. Rather it seeks to stall the age-related accumulation of molecular damage and functional deficits, boosting individual resilience against age-related decline. I call this preventive paradigm “ground-state prevention.” While new, ground-state prevention bears conceptual resemblance to forms of medical wisdom prominent in classic Galenic medicine, as well as in the Renaissance period., History and Philosophy of the Life Sciences, 43 (2), ISSN:0391-9714

In 2017 the Swiss federal government established the Swiss Personalized Health Network (SPHN), a nationally coordinated data infrastructure for genetic research. The SPHN advisory group on Ethical, Legal, and Social Implications (ELSI) was tasked with the creation of a recommendation to ensure ethically responsible reporting of genetic research findings to research participants in SPHN-funded studies. Following consultations with expert stakeholders, including geneticists, pediatricians, sociologists, university hospitals directors, patient representatives, consumer protection associations, and insurers, the ELSI advisory group issued its recommendation on “Reporting actionable genetic findings to research participants” in May 2020. In this paper we outline the development of this recommendation and the provisions it contains. In particular, we discuss some of its key features, namely: (1) that participation in SPHN-funded studies as a research subject is conditional to accepting that medically relevant genetic research findings will be reported; (2) that a Multidisciplinary Expert Panel (MEP) should be created to support researchers’ decision-making processes about reporting individual genetic research findings; (3) that such Multidisciplinary Expert Panel will make case-by-case decisions about whether to allow reporting of genetic findings, instead of relying on a pre-defined list of medically relevant variants; (4) that research participants shall be informed of the need to disclose genetic mutations when applying for private insurance, which may influence individual decisions about participation in research. By providing an account of the procedural background and considerations leading to the SPHN recommendation on “Reporting actionable genetic findings to research participants,” we seek to promote a better understanding of the proposed guidance, as well as to contribute to the global dialog on the reporting of genetic research findings.

Frontiers in Genetics, 11, ISSN:1664-8021

Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward., BMC Medical Informatics and Decision Making, 20, ISSN:1472-6947

Adaptive governance can help earn social license

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.

Health is a multi-dimensional concept, capturing how people feel and function. The broad concept of Active and Healthy Ageing was proposed by the World Health Organisation (WHO) as the process of optimizing opportunities for health to enhance quality of life as people age. It applies to both individuals and population groups. A universal Active and Healthy Ageing definition is not available and it may differ depending on the purpose of the definition and/or the questions raised. While the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has had a major impact, a definition of Active and Healthy Ageing is urgently needed. A meeting was organised in Montpellier, France, October 20-21, 2014 as the annual conference of the EIP on AHA Reference Site MACVIA-LR (Contre les Maladies Chroniques pour un Vieillissement Actif en Languedoc Roussillon) to propose an operational definition of Active and Healthy Ageing including tools that may be used for this. The current paper describes the rationale and the process by which the aims of the meeting will be reached. Health is a multi-dimensional concept, capturing how people feel and function. The broad concept of Active and Healthy Ageing was proposed by the World Health Organisation (WHO) as the process of optimizing opportunities for health to enhance quality of life as people age. It applies to both individuals and population groups. A universal Active and Healthy Ageing definition is not available and it may differ depending on the purpose of the definition and/or the questions raised. While the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has had a major impact, a definition of Active and Healthy Ageing is urgently needed. A meeting was organised in Montpellier, France, October 20-21, 2014 as the annual conference of the EIP on AHA Reference Site MACVIA-LR (Contre les Maladies Chroniques pour un Vieillissement Actif en Languedoc Roussillon) to propose an operational definition of Active and Healthy Ageing including tools that may be used for this. The current paper describes the rationale and the process by which the aims of the meeting will be reached.

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. "

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it., Journal of Bioethical Inquiry, 14 (4), ISSN:1872-4353

The implementation of the General Data Protection Regulation (GDPR) raises a series of challenges for scientific research, in particular for research that is dependent on data. This study comprehensively investigates the promises and challenges associated with the implementation of the GDPR in the scientific domain, with a special focus on the impact of the new rights and obligations enshrined in the GDPR on the design and conduct of scientific research. Furthermore, the study examines the adequacy of the GDPR's derogations for scientific research in terms of safeguarding scientific freedom and technological progress. The study also provides policy options that delineate a pathway towards enhancing rather than stifling research, and facilitating privacy-preserving data-driven research under the provisions of the GDPR.