Teaching the bioethics of information technologies and artificial intelligence in healthcare: Case-based learning for identifying and addressing ethical issues

Abstract

As applications of artificial intelligence (AI) integrate rapidly into healthcare, there is a pressing need for educational strategies to prepare various health professionals to identify, interrogate, and address AI-related ethical challenges. However, few pedagogical resources exist to support end users as they consider the ethical dimensions of healthcare AI. Involving highly technical elements and emerging regulatory structures, healthcare AI presents unique challenges to educators. The rapid pace of AI innovation and lack of transparency behind AI algorithms can limit opportunities to examine nuanced ethical themes related to algorithmic biases and validation. These limitations have far­ reaching implications when applied in practice, raising broader ethical concerns related to end-use and public trust, distribution of accountability for clinical decisions, and oversight of healthcare AI. While evidence supports the use of case-based learning in ethics education, the complexity of AI technologies demands careful consideration for how to integrate case-based learning into ethics education. In this paper, the authors describe pedagogical strategies used in an AI ethics course for healthcare professionals and biomedical scientists. Drawing on their experiences in teaching the course over three years, the authors describe the use of AI cases to promote consideration of ethical implications of AI among professionals whose careers may be impacted by the integration of AI-enabled technologies into healthcare. The authors close with reflections and lessons learned on the promises and challenges of graduate education as a tool in the responsible integration of AI into healthcare.

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