Implementing the human right to science in the regulatory governance of artificial intelligence in healthcare.

Artificial intelligence (AI) enables a medical device to optimize its performance through machine learning (ML), including the ability to learn from past experiences. In healthcare, ML is currently applied within controlled settings in devices to diagnose conditions like diabetic retinopathy without clinician input, for instance. In order to allow AI-based medical devices (AIMDs) to adapt actively to its data environment through ML, the current risk-based regulatory approaches are inadequate in facilitating this technological progression. Recent and innovative regulatory changes introduced to regulate AIMDs as a software, or 'software as a medical device' (SaMD), and the adoption of a total device/product-specific lifecycle approach (rather than one that is point-in-time) reflect a shift away from the strictly risk-based approach to one that is more collaborative and participatory in nature, and anticipatory in character. These features are better explained by a rights-based approach and consistent with the human right to science (HRS). With reference to the recent explication of the normative content of HRS by the Committee on Economic, Social and Cultural Rights of the United Nations, this paper explains why a rights-based approach that is centred on HRS could be a more effective response to the regulatory challenges posed by AIMDs. The paper also considers how such a rights-based approach could be implemented in the form of a regulatory network that draws on a 'common fund of knowledges' to formulate anticipatory responses to adaptive AIMDs. In essence, the HRS provides both the mandate and the obligation for states to ensure that regulatory governance of high connectivity AIMDs become increasingly collaborative and participatory in approach and pluralistic in substance.

Concordance of International Regulation of Pediatric Health Research.

OBJECTIVE: To assess the comparability of international ethics principles and practices used in regulating pediatric research as a first step in determining whether reciprocal deference for international ethics review is feasible. Prior studies by the authors focused on other aspects of international health research, such as biobanks and direct-to-participant genomic research. The unique nature of pediatric research and its distinctive regulation by many countries warranted a separate study. STUDY DESIGN: A representative sample of 21 countries was selected, with geographical, ethnic, cultural, political, and economic diversity. A leading expert on pediatric research ethics and law was selected to summarize the ethics review of pediatric research in each country. To ensure the comparability of the responses, a 5-part summary of pediatric research ethics principles in the US was developed by the investigators and distributed to all country representatives. The international experts were asked to assess and describe whether principles in their country and the US were congruent. Results were obtained and compiled in the spring and summer of 2022. RESULTS: Some of the countries varied in their conceptualization or description of one or more ethical principles for pediatric research, but overall, the countries in the study demonstrated a fundamental concordance. CONCLUSIONS: Similar regulation of pediatric research in 21 countries suggests that international reciprocity is a viable strategy.

The Genetic Discrimination Observatory: confronting novel issues in genetic discrimination.

Genetic discrimination (GD) is the differential or unfair profiling of an individual on the basis of genetic data. This article summarizes the actions of the Genetic Discrimination Observatory (GDO) in addressing GD and recent developments in GD since late 2020. It shows how GD can take many forms in today's rapidly evolving society.

Genetic discrimination: introducing the Asian perspective to the debate.

Our article aims to provide a comprehensive portrayal of how seven Asian jurisdictions have sought to address the challenge of genetic discrimination (GD) by presenting an analysis of the relevant legislation, policies, and practices. Based on our findings, policy discussion and action on preventing or mitigating GD have been narrowly framed in terms of employment, insurance, disability, marriage, and family planning. Except for South Korea, none of the jurisdictions we examined has adopted specific legislation to prevent GD. However, for Asia to truly benefit from its recent scientific and technological progress in genomics, we highlight the need for these jurisdictions to engage more proactively with the challenges of GD through a coordinated regulatory and governance mechanism.

Engaged genomic science produces better and fairer outcomes: an engagement framework for engaging and involving participants, patients and publics in genomics research and healthcare implementation.

Genomic science is increasingly central to the provision of health care. Producing and applying robust genomics knowledge is a complex endeavour in which no single individual, profession, discipline or community holds all the answers.  Engagement and involvement of diverse stakeholders can support alignment of societal and scientific interests, understandings and perspectives and promises better science and fairer outcomes. In this context we argue for F.A.I.R.E.R. data and data use that is Findable, Accessible, Interoperable, Reproducible, Equitable and Responsible. Yet there is a paucity of international guidance on how to engage publics, patients and participants in genomics. To support meaningful and effective engagement and involvement we developed an Engagement Framework for involving and engaging participants, patients and publics in genomics research and health implementation. The Engagement Framework is intended to support all those working in genomics research, medicine, and healthcare to deliberatively consider approaches to participant, patient and public engagement and involvement in their work. Through a series of questions, the Engagement Framework prompts new ways of thinking about the aims and purposes of engagement, and support reflection on the strengths, limitations, likely outcomes and impacts of choosing different approaches to engagement. To guide genomics activities, we describe four themes and associated questions for deliberative reflection: (i) fairness; (ii) context; (iii) heterogeneity, and (iv) recognising tensions and conflict. The four key components in the Engagement provide a framework to assist those involved in genomics to reflect on decisions they make for their initiatives, including the strategies selected, the participant, patient and public stakeholders engaged, and the anticipated goals. The Engagement Framework is one step in an actively evolving process of building genomics research and implementation cultures which foster responsible leadership and are attentive to objectives which increase equality, diversity and inclusion in participation and outcomes.

Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance.

Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.

Les progrès technologiques en matière de big data (un terme qui désigne de grandes quantités de données extrêmement variées, provenant de différentes sources et pouvant être traitées rapidement), de sciences de l'information et d'intelligence artificielle peuvent améliorer le fonctionnement du système de santé, mais aussi promouvoir des soins personnalisés et servir l'intérêt public. Néanmoins, ces technologies ne permettront pas de remplacer les composantes fondamentales du système de santé, comme le leadership éthique et la bonne gouvernance, ni d'éviter la nécessité de créer un environnement déontologique et réglementaire solide. Le présent document se penche sur la définition de cet environnement déontologique et réglementaire solide pour l'analyse des big data dans le domaine de l'assurance maladie, et fournit à titre d'exemple les mécanismes de protection et de participation qu'il convient d'instaurer. En premier lieu, imposer un cadre de gouvernance précis et efficace est essentiel au traitement des données. Des normes juridiques doivent être promulguées, tandis que les assureurs doivent être encouragés et incités à adopter une approche centrée sur l'humain, tant dans leur conception que dans leur utilisation de l'analyse des big data et de l'intelligence artificielle. Deuxièmement, il faut mettre en place un processus clair et responsable afin d'expliquer quels types d'informations sont susceptibles d'être employés et à quelles fins. Troisièmement, les personnes concernées doivent avoir la possibilité de déterminer de quelle manière leurs données personnelles sont gérées et régies, en étant activement impliquées dans ce processus. Et quatrièmement, les assureurs et les organes de gouvernance, dont les régulateurs et législateurs, doivent collaborer pour faire en sorte que l'analyse des big data basée sur l'intelligence artificielle soit correcte et transparente. À moins d'établir un environnement éthique, l'usage d'une telle analyse entraînera probablement la prolifération de systèmes de données non connectés, l'aggravation des inégalités actuelles ainsi qu'une perte de confiance et de fiabilité.

Los avances tecnológicos relativos a los macrodatos (es decir, grandes cantidades de datos muy variados de muchas fuentes diversas que pueden procesarse rápidamente), las ciencias de los datos y la inteligencia artificial pueden mejorar las funciones del sistema sanitario y promover la atención personalizada y el bien público. No obstante, estas tecnologías no sustituirán los componentes fundamentales del sistema sanitario, como el liderazgo ético y la gobernanza, ni evitarán la necesidad de un entorno ético y normativo sólido. En el presente documento se examina cómo podría ser un entorno ético y normativo sólido para el análisis de macrodatos en el ámbito de los seguros médicos, y se describen ejemplos de mecanismos de protección y participación que deberían establecerse. En primer lugar, es fundamental contar con un marco claro y eficaz de gestión de datos. Es necesario promulgar normas jurídicas y alentar e incentivar a las aseguradoras para que adopten un enfoque centrado en el ser humano en el diseño y la aplicación de análisis de macrodatos e inteligencia artificial. En segundo lugar, es necesario un proceso claro y responsable para explicar cómo y qué información se puede utilizar. En tercer lugar, se debe facultar a las personas cuyos datos puedan ser utilizados mediante su participación activa en la determinación de cómo se pueden gestionar y regular sus datos personales. En cuarto lugar, las aseguradoras y los órganos de gobierno, incluidos los reguladores y los responsables de formular políticas, deben colaborar para garantizar que los análisis de macrodatos basados en la inteligencia artificial que se elaboren sean transparentes y precisos. A menos que exista un entorno ético adecuado, el uso de esos análisis probablemente contribuirá a la proliferación de sistemas de datos sin conexión, empeorará las desigualdades existentes y reducirá la fiabilidad y la confianza.

Correction to: Capacity Building: Continuity and Change.

[This corrects the article DOI: 10.1007/s41649-019-00108-z.].

Legal Uncertainty-The Gray Area around Substandard Medicines: Where Public Health Meets Law.

A vicious circle links lack of equitable access to health to the supply of poor-quality medicines, which amount to one-tenth of medicines available in low- and middle-income countries. The WHO introduced a new, public health-focused definition of substandard and falsified (SF) medicines, which offers opportunities for governments to broaden the scope of interventions to combat poor-quality medicines. At the same time, translating it into legal and regulatory measures may be challenging because this definition is not free of ambiguity (in that, there is a gray area between intentionally falsified and unintentional substandard medicines), and some countries may not have appropriate regulatory mechanisms/jurisdictions in place. The focus of the article is to consider what a public health-informed legal and regulatory environment could look like in light of WHO's SF definition and propose appropriate measures to put it into effect. We present a "legal levers matrix" that may assist legislators and policymakers evaluate the adequacy of measures (i.e., criminal, civil, and administrative mechanisms) to address the problem of poor-quality medicines, particularly in terms of their configuration. In addition, this matrix underscores the importance of fostering dialogue between medical/public health and the legal/regulatory communities and to develop alternative/complementary solutions, including regulatory strengthening and nonpunitive actions. Substandard and falsified medicines arise from the interplay between societies, economies, and behaviors: effective regulation is necessary to disincentivize the production and/or supply of SF medicines, whereas health systems should strive to provide affordable medicines to all levels of society.

Has the biobank bubble burst? Withstanding the challenges for sustainable biobanking in the digital era.

Biobanks have been heralded as essential tools for translating biomedical research into practice, driving precision medicine to improve pathways for global healthcare treatment and services. Many nations have established specific governance systems to facilitate research and to address the complex ethical, legal and social challenges that they present, but this has not lead to uniformity across the world. Despite significant progress in responding to the ethical, legal and social implications of biobanking, operational, sustainability and funding challenges continue to emerge. No coherent strategy has yet been identified for addressing them. This has brought into question the overall viability and usefulness of biobanks in light of the significant resources required to keep them running. This review sets out the challenges that the biobanking community has had to overcome since their inception in the early 2000s. The first section provides a brief outline of the diversity in biobank and regulatory architecture in seven countries: Australia, Germany, Japan, Singapore, Taiwan, the UK, and the USA. The article then discusses four waves of responses to biobanking challenges. This article had its genesis in a discussion on biobanks during the Centre for Health, Law and Emerging Technologies (HeLEX) conference in Oxford UK, co-sponsored by the Centre for Law and Genetics (University of Tasmania). This article aims to provide a review of the issues associated with biobank practices and governance, with a view to informing the future course of both large-scale and smaller scale biobanks.