Large language models (LLMs) such as ChatGPT or Google’s Bard have shown significant performance on a variety of text-based tasks, such as summarization, translation, and even the generation of new...

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

Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the “empirical turn” in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased (...) use of empirical methodologies from social sciences, the proliferation of health-related subtypes of technology ethics might have a comparable impact on current bioethical research. This systematic journal review analyses the reporting of ethical frameworks and non-empirical methods in argument-based research articles on digital technologies in medicine, health care and public health that have been published in high-impact bioethics journals. We focus on articles reporting non-empirical research in original contributions. Our aim is to describe currently used methods for the ethical analysis of ethical issues regarding the application of digital technologies in medicine, health care and public health. We confine our analysis to non-empirical methods because empirical methods have been well-researched elsewhere. Finally, we discuss our findings against the background of established methods for health technology assessment, the lack of a typology for non-empirical methods as well as conceptual and methodical change in bioethics. Our descriptive results may serve as a starting point for reflecting on whether current ethical frameworks and non-empirical methods are appropriate to research ethical issues deriving from the application of digital technologies in medicine, health care and public health. (shrink)

In their recent article, Porsdam Mannet alpropose to share biomedical research data more widely, securely and efficiently using blockchain technologies. 1 They present compelling arguments for how the blockchain presents both a technological innovation, and a deontologically grounded policy innovation to traditional research consent. Their proposal can be read in conversation with a rich body of evidence to suggest current consent processes are problematic on at least one of tripartite bases in biomedical research: that it be fully informed. This response (...) attempts to further the author’s discussion of social justice discourse in, and of their proposed prosent model to enhance engagement among under-represented and vulnerable populations in research, specifically. Motivating this response is the view that advancing technological capabilities is no doubt necessary, but on its own insufficient to reinvigorate distributive, procedural and social justice as guiding principles for con/prosent processes. I offer three pros and cons to consider in effort to deepen the model’s commitments to social justice to historically marginalised groups in the biomedical research enterprise. (shrink)

Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata (...) information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples. (shrink)