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The role of patients in medical encounters —and the labels they assigned to them or preferred by them— has been a subject of debate in medical ethics over the last few decades. While historically the arguments in favor of a certain label have centered on an increased importance of patient autonomy and the commodification of healthcare services, the integration of artificial intelligence (AI) into healthcare introduces new complexities. AI tools mediate relationships at epistemic, material, and relational levels, introducing uncertainties about the roles and identities of people seeking and receiving care. This paper explores the implications of the labels of ‘user’, ‘client’ and ‘consumer’ through a relational lens, arguing that terminological choices are not neutral: they can both reflect and reshape the underlying moral and institutional structures of the provision of medical care. The conclusion calls for a critical and deliberate conversation about how the choice of a label reveals the kinds of relationships we prioritize in medicine, and how AI mediation is shaping these choices.

This book seeks to navigate between the optimism that has arisen from the promise of the potential of machine learning (ML) in healthcare, and the lack of clarity about what realistic risks and benefits we can foresee. Its main aim is to develop a relational, rights-based normative approach to evaluating the distribution of burdens and benefits of implementing ML in medical diagnosis. This framework, called the "Ecosystem of Moral Constellations", assumes that every person has an equal claim to the fundamental rights necessary to lead one’s life, but recognizes that there may be conflicting interests that risk violating or infringing the rights of an individual or individuals, and that therefore an assessment of these tensions requires a situational prioritization of certain rights over others. This framework proposes to consider the normative relevance of relationships at different points of moral engagement to assess the potential tensions between these burdens and benefits of these technologies. The author argues that decisions about the implementation of AI systems require more than an assessment of technical feasibility. Instead, it is imperative to consider the different normative goals and interests of the actors involved, the material capabilities of the tools, and the role they should play in the clinical workflow.