人工智能需要数据:访问意大利数据库以训练人工智能所面临的挑战

IF 1.3 Q3 ETHICS
Ciara Staunton, Roberta Biasiotto, Katharina Tschigg, Deborah Mascalzoni
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引用次数: 0

摘要

人口生物库是支持研究的日益重要的基础设施,也将成为提供个性化医疗所急需的资源。人工智能(AI)系统可以快速处理和交叉链接大量数据,不仅可用于提高研究能力,还可根据健康状况帮助进行复杂的疾病诊断和预测。因此,人工智能有可能在个性化医疗中发挥关键作用,而生物库可以提供大量与健康人群相关的必要基线数据,从而促进人工智能工具的开发。要开发这些工具,就需要获取个人数据,尤其是敏感数据。这些数据可以从生物库中获取。生物库是一种宝贵的研究资源,但获取和使用生物库中的数据会引发一系列法律、伦理和社会问题(ELSI)。这包括对样本和数据的收集、储存、使用和共享进行管理的适当同意,以及对样本和数据的二次使用进行监督的适当管理模式。生物库已经开发了新的同意模式和管理工具,以解决与 ELSI 相关的一些问题。在本文中,我们将考虑这种管理框架是否能使生物库数据的使用成为可能,从而开发人工智能。由于意大利是欧洲对基因数据使用限制最严格的监管框架之一,我们对意大利的监管框架进行了研究。我们还研究了欧洲健康数据空间(EHDS)下的修改建议。最后,我们认为目前的监管框架存在偏差,如果不加以解决,意大利生物库中用于训练人工智能的数据将受到严重限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Needs Data: Challenges Accessing Italian Databases to Train AI

Population biobanks are an increasingly important infrastructure to support research and will be a much-needed resource in the delivery of personalised medicine. Artificial intelligence (AI) systems can process and cross-link very large amounts of data quickly and be used not only for improving research power but also for helping with complex diagnosis and prediction of diseases based on health profiles. AI, therefore, potentially has a critical role to play in personalised medicine, and biobanks can provide a lot of the necessary baseline data related to healthy populations that will enable the development of AI tools. To develop these tools, access to personal data, and in particular, sensitive data, is required. Such data could be accessed from biobanks. Biobanks are a valuable resource for research but accessing and using the data contained within such biobanks raise a host of legal, ethical, and social issues (ELSI). This includes the appropriate consent to manage the collection, storage, use, and sharing of samples and data, and appropriate governance models that provide oversight of secondary use of samples and data. Biobanks have developed new consent models and governance tools to enable access that address some of these ELSI-related issues. In this paper, we consider whether such governance frameworks can enable access to biobank data to develop AI. As Italy has one of the most restrictive regulatory frameworks on the use of genetic data in Europe, we examine the regulatory framework in Italy. We also look at the proposed changes under the European Health Data Space (EHDS). We conclude by arguing that currently, regulatory frameworks are misaligned and unless addressed, accessing data within Italian biobanks to train AI will be severely limited.

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来源期刊
CiteScore
6.20
自引率
3.40%
发文量
32
期刊介绍: Asian Bioethics Review (ABR) is an international academic journal, based in Asia, providing a forum to express and exchange original ideas on all aspects of bioethics, especially those relevant to the region. Published quarterly, the journal seeks to promote collaborative research among scholars in Asia or with an interest in Asia, as well as multi-cultural and multi-disciplinary bioethical studies more generally. It will appeal to all working on bioethical issues in biomedicine, healthcare, caregiving and patient support, genetics, law and governance, health systems and policy, science studies and research. ABR provides analyses, perspectives and insights into new approaches in bioethics, recent changes in biomedical law and policy, developments in capacity building and professional training, and voices or essays from a student’s perspective. The journal includes articles, research studies, target articles, case evaluations and commentaries. It also publishes book reviews and correspondence to the editor. ABR welcomes original papers from all countries, particularly those that relate to Asia. ABR is the flagship publication of the Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore. The Centre for Biomedical Ethics is a collaborating centre on bioethics of the World Health Organization.
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