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THE ARTIFICIAL INTELLIGENCE (AI) sensation ChatGPT, and rivals such as BLOOM and Stable Diffusion, are large language models for consumers. ChatGPT has caused particular delight since it first appeared in [...]

Drawing from real-life scenarios and insights shared at the RAISE (Responsible AI for Social and Ethical Healthcare) conference, we highlight the critical need for AI in health care (AIH) to primarily benefit patients and address current shortcomings in health care systems such as medical errors and access disparities.

Pediatric health care providers caring for patients and families with genetic disease will encounter a range of ethical issues. These include traditional pediatric health care issues, such as surrogate decision making and end-of-life care. Genetic testing raises the importance of informed consent for potential risks that move beyond the oft discussed physical risks and into longer term concepts such as psychological impact, privacy and potential discrimination. Predictive testing in childhood also raises questions of whether the child has an autonomy interest in delaying testing until they have decision making capacity to do so on their own. And finally, treatments including gene therapies and gene editing, may raise issues of identity for families dealing with genetic disease.

Fuelled by adaptations to clinical trial implementation during the COVID-19 pandemic, decentralised clinical trials are burgeoning. Decentralised clinical trials involve many digital tools to facilitate research without physical contact between research teams and participants at various stages, such as recruitment, enrolment, informed consent, administering study interventions, obtaining patient-reported outcome measures, and safety monitoring. These tools can provide ways of ensuring participants' safety and research integrity, while sometimes reducing participant burden and trial cost. Research sponsors and investigators are interested in expanding the use of decentralised clinical trials. The US Food and Drug Administration and other regulators worldwide have issued guidance on how to implement such adaptations. However, there has been little focus on the distinct ethical challenges these trials pose. In this Health Policy report, which is informed by both traditional research ethics and digital ethics frameworks, we group the related ethical issues under three areas requiring increased ethical vigilance: participants' safety and rights, scientific validity, and ethics oversight. Our aim is to describe these issues, offer practical means of addressing them, and prompt the delineation of ethical standards for decentralised trials.

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 bioethics digital bioethics.

Population-level biomedical research offers new opportunities to improve population health, but also raises new challenges to traditional systems of research governance and ethical oversight. Partly in response to these challenges, various models of public involvement in research are being introduced. Yet, the ways in which public involvement should meet governance challenges are not well understood. We conducted a qualitative study with 36 experts and stakeholders using the World Café method to identify key governance challenges and explore how public involvement can meet these challenges. This brief report discusses four cross-cutting themes from the study: the need to move beyond individual consent; issues in benefit and data sharing; the challenge of delineating and understanding publics; and the goal of clarifying justifications for public involvement. The report aims to provide a starting point for making sense of the relationship between public involvement and the governance of population-level biomedical research, showing connections, potential solutions and issues arising at their intersection. We suggest that, in population-level biomedical research, there is a pressing need for a shift away from conventional governance frameworks focused on the individual and towards a focus on collectives, as well as to foreground ethical issues around social justice and develop ways to address cultural diversity, value pluralism and competing stakeholder interests. There are many unresolved questions around how this shift could be realised, but these unresolved questions should form the basis for developing justificatory accounts and frameworks for suitable collective models of public involvement in population-level biomedical research governance.

The increased use of big data in the medical field has shifted the way in which biomedical research is designed and carried out. The novelty of techniques and methods brought by big data research brings new challenges to institutional review boards (IRBs). Yet it is unclear if IRBs should be the responsible oversight bodies for big data research and, if so, which criteria they should use. A large but heterogenous set of ethics guidelines and normative responses have emerged to address these issues. In this study, we conducted a scoping review of soft‐law documents and guidelines with the aim of assessing ongoing normative efforts that are proliferating in this domain. We also synthesize a set of recurrent guidelines that could work as a baseline to create a harmonized process for big data research ethics.

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.

Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilising digital tools for public health. To help scientists and policy makers to navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications that are in use. These include proximity and contact tracing, symptom monitoring, quarantine control, and flow modelling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, recognising the need for practical guidance, we propose a navigation aid for policy makers and other decision makers for the ethical development and use of digital public health tools.

While genetic testing may be the gateway to the future of medicine, it also poses challenges for individuals, especially in terms of differentiated treatments on the basis of their genetic characteristics. The fear of unwanted disclosure to insurers and the possibility of genetic discrimination can hamper the recruitment of individuals for clinical research that involves genetic testing. Precision medicine initiatives, such as All of Us, are proliferating in the United States. In order to succeed, however, they must ensure that the millions of Americans recruited to share their genetic data are not penalized with regard to life, disability and long-term insurance coverage. In this Perspective, we discuss several initiatives adopted by countries around the world, such as the United Kingdom and France, that better balance the interests of insurers and research subjects, and explain how the United States might learn from them. We call for regulatory and industry leadership to come together to establish a voluntary moratorium on insurance pricing with the aim of protecting research participants.

Large-scale collection of data could help curb the COVID-19 pandemic, but it should not neglect privacy and public trust. Best practices should be identified to maintain responsible data-collection and data-processing standards at a global scale.

Regulators must consider the potential benefits of using genomic information to advance clinical medicine.