Why applied AI ethics in healthcare requires the integration of empirical inquiry and normative ethical analysis

Abstract

AI technologies in healthcare are increasingly subject to ethical scrutiny, often structured around high‑level ethical principles such as fairness, transparency, and accountability. While these principles are widely endorsed, their application frequently remains abstract, underspecified, and detached from the empirical contexts in which AI systems are developed and used. This paper argues that empirical ethics provides a necessary foundation for applied AI ethics in healthcare by enabling the ethically relevant contextualisation of normative principles through a reciprocal relationship between empirical and normative ethical analysis. In doing so, the paper addresses a persistent gap between ethical principles and empirical research in AI ethics by showing how these domains can be methodologically integrated rather than treated as separate or sequential. Drawing on empirical examples and our own experience from medical AI research and stakeholder engagement, we illustrate how empirical inquiry and normative ethical analysis, can work together to clarify the meaning of ethical values such as fairness, identify ethically salient risks and trade‑offs, and support ethically robust decision‑making across the AI lifecycle, including research, development, and procurement. We argue that without systematic and methodologically grounded empirical engagement, AI ethics risks becoming either toothless or merely procedural, or sliding into forms of ethical relativism in which empirical findings are treated as normatively sufficient. The article contributes to ongoing debates on how to operationalise AI ethics and highlights the role that empirical ethics can play in bridging ethical theory, law, and practice.