Ethical and epistemic implications of artificial intelligence in medicine: a stakeholder-based assessment

Abstract As artificial intelligence (AI) technologies become increasingly embedded in high-stakes fields such as healthcare, ethical and epistemic considerations raise the need for evaluative frameworks to assess their societal impacts across multiple dimensions. This paper uses the ethical-epistemic matrix (EEM), a structured framework that integrates both ethical and epistemic principles, to evaluate medical AI applications more comprehensively. Building on the ethical principles of well-being, autonomy, justice, and explicability, the matrix introduces epistemic principles—accuracy, consistency, relevance, and instrumental efficacy—that assess AI’s role in knowledge production. This dual approach enables a nuanced assessment that reflects the diverse perspectives of stakeholders within the medical field—patients, clinicians, developers, the public, and health policy-makers—who assess AI systems differently based on distinct interests and epistemic goals. Although the EEM has been outlined conceptually before, no published research paper has yet used it explore the ethical and epistemic implications arising in its key intended application domain of AI in medicine. Through a systematic demonstration of the EEM as applied to medical AI, this paper argues that it encourages a broader understanding of AI’s implications and serves as a valuable methodological tool for evaluating future uses. This is illustrated with the case study of AI systems in sleep apnea detection, where the EEM highlights the ethical trade-offs and epistemic challenges that different stakeholders may perceive, which can be made more concrete if the tool is embedded in future technical projects.