期刊
AMERICAN JOURNAL OF BIOETHICS
卷 22, 期 5, 页码 8-22出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/15265161.2021.2013977
关键词
Ethics committees; health care delivery; human subjects research; informed consent; IRB (Institutional Review Board); research ethics
This paper presents a comprehensive research ethics framework that can be applied to the investigation of machine learning research at different stages. By connecting each stage to literature and ethical justifications, and adapting to the characteristics of machine learning, ethical rigor and individual protection can be maintained.
The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications.
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