Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2024-04-10 DOI:10.1146/annurev-biodatasci-102623-104553

Carole A Federico, Artem A. Trotsyuk

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Abstract

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

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生物医学数据科学、人工智能与伦理：面对爆炸式增长的挑战。

生物医学数据科学和人工智能（AI）的进步正在深刻改变医疗保健的格局。本文回顾了随着人工智能技术的发展而产生的伦理问题，包括对隐私、数据安全、同意和公正的威胁，因为它们涉及到组织和数据的捐献者。文章还考虑了更广泛的社会义务，包括评估人工智能研究在生物医学中的意外后果的重要性。此外，本文还强调了在监管框架不统一的背景下人工智能快速发展所带来的挑战，呼吁采用全球方法来解决数据滥用、意外监控以及公平分配人工智能惠益和负担等问题。最后，我们提出了一些解决这些伦理难题的潜在方案。也就是说，讨论了倡导一种协作、知情和灵活的监管方法的优点，这种方法可以平衡创新与个人权利和公共福利，促进一个值得信赖的人工智能驱动的医疗保健生态系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

期刊介绍： The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.