AI and personalized medicine in healthcare: algorithmic normativity and practice configurations in danish healthcare education

Abstract Personalized medicine (PM) is reshaping healthcare by introducing new technologies, competencies, and normative tensions. This qualitative study explores how healthcare professionals, educators, and planners involved in a Danish master’s program in PM articulate and negotiate expectations, concerns, and interpretations of artificial intelligence (AI)’s role in future clinical practice. Drawing on 24 interviews, document analysis, and 28 h of classroom observations, we apply Shove’s practice theory to examine how changes in materials (technologies), competences (skills and collaboration), and meanings (professional identity and ethical values) interact in the reconfiguration of healthcare work. We emphasize that these transformations occur through making and breaking links between elements and recursive integration, producing new practice trajectories rather than isolated technological adoption. Findings indicate that PM challenges traditional roles and responsibilities, prompting both uncertainty and innovation. Participants express ambivalence toward algorithmic decision making, citing concerns about transparency, accountability, and the erosion of clinical judgment. Ethical dilemmas emerge around data ownership, informed consent, secondary findings, and the right not to know, highlighting how algorithmic systems increasingly participate in decisions that were previously governed by human professionals. We conceptualize this shift as algorithmic normativity , the process through which algorithms not only support but actively shape normative frameworks in healthcare. The analysis shows how these practice configurations drive normative change and points to the importance of understanding technological transformation as a matter of reconfiguring practice linkages rather than individual adoption.