Patient self-management of disease is increasingly supported by technologies that can monitor a wide range of behavioural and biomedical parameters. Incorporated into everyday devices such as cell phones and clothes, these technologies become integral to the psychosocial aspects of everyday life. Many technologies are likely to be marketed directly to families with ill members, and families may enlist the support of clinicians in shaping use. Current ethical frameworks are mainly conceptualised from the perspective of caregivers, researchers, developers and regulators in (...) order to ensure the ethics of their own practices. This paper focuses on families as autonomous decision-makers outside the regulated context of healthcare. We discuss some morally relevant issues facing families in their decisions to monitor the health-related behaviours of loved ones. An example – remote parental monitoring of adolescent blood glucose – is presented and discussed through the lens of two contrasting accounts of ethics; one reflecting the predominant focus on health outcomes within the health technology assessment (HTA) framework and the other that attends to the broader sociocultural contexts shaping technologies and their implications. Issues discussed include the focus of assessments, informed consent and child assent, and family co-creation of system characteristics and implications. The parents’ decisions to remotely monitor their child has relational implications that are likely to influence conflict levels and thus also health outcomes. Current efforts to better integrate outcome assessments with social and ethical assessments are particularly relevant for informed decision-making about health monitoring technologies in families. (shrink)

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which (...) explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care. (shrink)