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DDC: 004 × Subject: Research ×

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    The genomic data deficit : On the need to inform research subjects of the informational content of their genomic sequence data in consent for genomic research
    (Amsterdam [u.a.] : Elsevier Science, 2020) Hallinan, Dara
    Research subject consent plays a significant role in the legitimation of genomic research in Europe – both ethically and legally. One key criterion for any consent to be legitimate is that the research subject is ‘informed’. This criterion implies that the research subject is given all relevant information to allow them to decide whether engaging with a genomic research infrastructure or project would be normatively desirable and whether they wish to accept the risks associated with engagement. This article makes the normative argument that, in order to be truly ‘informed’, the research subject should be provided with information on the informational content of their genomic sequence data. Information should be provided, in the first instance, prior to the initial consent transaction, and should include: information on the fact that genomic sequence data will be collected and processed, information on the types of information which can currently be extracted from sequence data and information on the uncertainties surrounding the types of information which may eventually be extractable from sequence data. Information should also be provided, on an ongoing basis, as relevant and necessary, throughout the research process, and should include: information on novel information which can be extracted from sequence data and information on the novel uses and utility of sequence data. The article argues that current elaborations of ‘informed’ consent fail to adequately address the requirements set out in the normative argument and that this inadequacy constitutes an issue in need of a solution. The article finishes with a set of observations as to the fora best suited to deliver a solution and as to the substantive content of a solution. © 2020 The Authors
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    Bridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol
    (Hannover : Technische Informationsbibliothek, 2024-02) Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Grönke, Ana; Seletkov, Dmitrii; Kotter, Elmar; Nensa, Felix; Wehrle, Julius; Kaufmes, Kevin; Scherer, Lucas; Nolden, Marco; Boeker, Martin; Schmidt, Marvin; Pelka, Obioma; Braren, Rickmer; Stump, Shura-Roman; Graetz, Teresa; Pogarell, Tobias; Susetzky, Tobias; Wieland, Tobias; Parmar, Vicky; Wang, Yuanbin
    Artificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.