PDF(Pubmed)
Abstract：
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.
摘要：
大数据和人工智能是医疗领域的关键要素，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时，促进个人定制的预防和治疗干预措施的进展。这些应用程序属于当前的数据密集型研究实践。而结合成像，病态,基因组，需要临床数据来训练算法，以实现这些技术的全部潜力，生物银行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”已经越来越多地重组了主要基础设施，通常是为生物样本和相关数据创建的，作为主要的数据基础设施。随着时间的推移，在解决协调和标准化等相关问题方面，这些问题已经发展和多样化。但也同意做法和风险评估。根据数据通报，越来越多地使用基于人工智能的技术，标志着生命科学和医学大数据研究的前沿发展，带来了新的问题和问题以及机遇。在安全的健康数据环境中，例如欧洲健康数据空间，正在制作中，我们认为，这种元基础设施可以从生物样本的经验和演变中受益，还有人工智能在医学领域的现状，关于善政，社会方面和实践，以及对数据实践的批判性思考，这可以有助于这种元基础设施的可信度。
