Unlocking the potential of big data and AI in medicine: insights from biobanking.

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Ethical, legal, and social implications in research biobanking: A checklist for navigating complexity.

Biobanks' activity is based not only on securing the technology of collecting and storing human biospecimen, but also on preparing formal documentation that will enable its safe use for scientific research. In that context, the issue of informed consent, the reporting of incidental findings and the use of Transfer Agreements remain a vast challenge. This paper aims to offer first-hand tangible solutions on those issues in the context of collaborative and transnational biobanking research. It presents a four-step checklist aiming to facilitate researchers on their compliance with applicable legal and ethical guidelines, when designing their studies, when recruiting participants, when handling samples and data, and when communicating research results and incidental findings. Although the paper reflects the outcomes of the H2020 B3Africa project and examines the transfers from and to the EU as a case study, it presents a global checklist that can be used beyond the EU.

Post-identifiability in changing sociotechnological genomic data environments.

Data practices in biomedical research often rely on standards that build on normative assumptions regarding privacy and involve 'ethics work.' In an increasingly datafied research environment, identifiability gains a new temporal and spatial dimension, especially in regard to genomic data. In this paper, we analyze how genomic identifiability is considered as a specific data issue in a recent controversial case: publication of the genome sequence of the HeLa cell line. Considering developments in the sociotechnological and data environment, such as big data, biomedical, recreational, and research uses of genomics, our analysis highlights what it means to be (re-)identifiable in the postgenomic era. By showing how the risk of genomic identifiability is not a specificity of the HeLa controversy, but rather a systematic data issue, we argue that a new conceptualization is needed. With the notion of post-identifiability as a sociotechnological situation, we show how past assumptions and ideas about future possibilities come together in the case of genomic identifiability. We conclude by discussing how kinship, temporality, and openness are subject to renewed negotiations along with the changing understandings and expectations of identifiability and status of genomic data.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Biobanking and risk assessment: a comprehensive typology of risks for an adaptive risk governance.

Biobanks act as the custodians for the access to and responsible use of human biological samples and related data that have been generously donated by individuals to serve the public interest and scientific advances in the health research realm. Risk assessment has become a daily practice for biobanks and has been discussed from different perspectives. This paper aims to provide a literature review on risk assessment in order to put together a comprehensive typology of diverse risks biobanks could potentially face. Methodologically set as a typology, the conceptual approach used in this paper is based on the interdisciplinary analysis of scientific literature, the relevant ethical and legal instruments and practices in biobanking to identify how risks are assessed, considered and mitigated. Through an interdisciplinary mapping exercise, we have produced a typology of potential risks in biobanking, taking into consideration the perspectives of different stakeholders, such as institutional actors and publics, including participants and representative organizations. With this approach, we have identified the following risk types: economic, infrastructural, institutional, research community risks and participant's risks. The paper concludes by highlighting the necessity of an adaptive risk governance as an integral part of good governance in biobanking. In this regard, it contributes to sustainability in biobanking by assisting in the design of relevant risk management practices, where they are not already in place or require an update. The typology is intended to be useful from the early stages of establishing such a complex and multileveled biomedical infrastructure as well as to provide a catalogue of risks for improving the risk management practices already in place.

(Re)Configuring Hybrid Meetings: Moving from User-Centered Design to Meeting-Centered Design.

Despite sophisticated technologies for representational fidelity in hybrid meetings, in which co-located and remote participants collaborate via video or audio, meetings are still often disrupted by practical problems with trying to include remote participants. In this paper, we use micro-analysis of three disruptive moments in a hybrid meeting from a global software company to unpack blended technological and conversational practices of inclusion and exclusion. We argue that designing truly valuable experiences for hybrid meetings requires moving from the traditional, essentialist, and perception-obsessed user-centered design approach to a phenomenological approach to the needs of meetings themselves. We employ the metaphor of 'configuring the meeting' to propose that complex ecologies of people, technology, spatial, and institutional organization must be made relevant in the process of design.

Exonic duplication CNV of NDRG1 associated with autosomal-recessive HMSN-Lom/CMT4D.

PURPOSE: Copy-number variations as a mutational mechanism contribute significantly to human disease. Approximately one-half of the patients with Charcot-Marie-Tooth (CMT) disease have a 1.4 Mb duplication copy-number variation as the cause of their neuropathy. However, non-CMT1A neuropathy patients rarely have causative copy-number variations, and to date, autosomal-recessive disease has not been associated with copy-number variation as a mutational mechanism. METHODS: We performed Agilent 8 × 60 K array comparative genomic hybridization on DNA from 12 recessive Turkish families with CMT disease. Additional molecular studies were conducted to detect breakpoint junctions and to evaluate gene expression levels in a family in which we detected an intragenic duplication copy-number variation. RESULTS: We detected an ~6.25 kb homozygous intragenic duplication in NDRG1, a gene known to be causative for recessive HMSNL/CMT4D, in three individuals from a Turkish family with CMT neuropathy. Further studies showed that this intragenic copy-number variation resulted in a homozygous duplication of exons 6-8 that caused decreased mRNA expression of NDRG1. CONCLUSION: Exon-focused high-resolution array comparative genomic hybridization enables the detection of copy-number variation carrier states in recessive genes, particularly small copy-number variations encompassing or disrupting single genes. In families for whom a molecular diagnosis has not been elucidated by conventional clinical assays, an assessment for copy-number variations in known CMT genes might be considered.