Faculty, Staff and Student Publications
Publication Date
12-1-2024
Journal
JAMIA Open
Abstract
OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.
MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.
RESULTS DISCUSSION AND CONCLUSION: Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.
Keywords
artificial intelligence, ethics, healthcare
DOI
10.1093/jamiaopen/ooae108
PMID
39553826
PMCID
PMC11565898
PubMedCentral® Posted Date
11-15-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Artificial Intelligence and Robotics Commons, Bioethics and Medical Ethics Commons, Bioinformatics Commons, Biomedical Informatics Commons, Data Science Commons