Akyüz Kaya, Cano Abadía Mónica, Goisauf Melanie, Mayrhofer Michaela Th
Department of ELSI Services and Research, BBMRI-ERIC, Graz, Austria.
Front Med (Lausanne). 2024 Jan 31;11:1336588. doi: 10.3389/fmed.2024.1336588. eCollection 2024.
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.
大数据和人工智能是医学领域的关键要素，因为它们有望提高诊断和治疗的准确性和效率，特别是在识别生物医学相关模式方面，促进朝着个性化定制的预防和治疗干预措施发展。这些应用属于当前数据密集型的研究实践。虽然需要结合成像、病理、基因组和临床数据来训练算法，以充分发挥这些技术的潜力，但生物样本库通常是数据共享和数据流的关键基础设施。在本文中，我们认为生命科学中的“数据转向”越来越多地将主要为生物样本和相关数据创建的基础设施重新构建为主要的数据基础设施。随着时间的推移，这些基础设施在解决诸如协调和标准化等相关问题方面不断发展和多样化，同时也涉及同意做法和风险评估。与数据化一致，基于人工智能的技术的更多使用标志着生命科学和医学大数据研究前沿的当前发展，这在带来机遇的同时也引发了新的问题和担忧。在诸如欧洲健康数据空间这样的安全健康数据环境正在形成之际，我们认为这样的元基础设施既可以从生物样本库的经验和发展中受益，也可以从医学人工智能在良好治理、社会层面和实践以及对数据实践的批判性思考等方面的现状中受益，这有助于提高此类元基础设施的可信度。
