Alessandro Blasimme

Digital technologies hold promise to modernize healthcare. Such opportunity should be leveraged also to address the needs of rapidly ageing populations. Against this backdrop, this paper examines the use of wearable devices for promoting healthy ageing. Previous work has assessed the prospects of digital technologies for health promotion and disease prevention in older adults. However, to our knowledge, ours is one of the first attempts to specifically address the use of wearables for healthy ageing, and to offer ethical insights for assessing the prospects of leveraging wearable devices in this context. We provide an analysis of the considerable opportunities associated with the use of wearables for healthy ageing, with a focus on the five domains of intrinsic capacity: locomotion, sensory functions, psychological aspects, cognition, and vitality. We then highlight current limitations and ethical challenges of such approach to healthy ageing, including issues related to access, inclusion, privacy, surveillance, autonomy, and regulation. We conclude by discussing the implications of our analysis in light of current debates on the ethics of digital health, and suggest measures to address the identified challenges.

Precision medicine promises to develop diagnoses and treatments that take individual variability into account. According to most specialists, turning this promise into reality will require adapting the established framework of clinical research ethics, and paying more attention to participants’ attitudes towards sharing genotypic, phenotypic, lifestyle data and health records, and ultimately to their desire to be engaged as active partners in medical research.Notions such as participation, engagement and partnership have been introduced in bioethics debates concerning genetics and large-scale biobanking to broaden the focus of discussion beyond individual choice and individuals’ moral interests. The uptake of those concepts in precision medicine is to be welcomed. However, as data and medical information from research participants in precision medicine cohorts will be collected on an individual basis, translating a participatory approach in this emerging area may prove cumbersome. Therefore, drawing on Joseph Raz’s perfectionism, we propose a principle of respect for autonomous agents that, we reckon, can address many of the concerns driving recent scholarship on partnership and public participation, while avoiding some of the limitations these concept have in the context of precision medicine. Our approach offers a normative clarification to how becoming partners in precision is compatible with retaining autonomy.Realigning the value of autonomy with ideals of direct engagement, we show, can provide adequate normative orientation to precision medicine; it can do justice to the idea of moral pluralism by stressing the value of moral self-determination: and, finally, it can reconcile the notion of autonomy with other more communitarian values such as participation and solidarity.

What has been called the new mechanistic philosophy conceives of mechanisms as the main providers of biological explanation. We draw on the characterization of the p53 gene in molecular oncology, to show that explaining a biological phenomenon implies instead a dynamic interaction between the mechanistic level—rendered at the appropriate degree of ontological resolution—and far more general explanatory tools that perform a fundamental epistemic role in the provision of biological explanations. We call such tools “explanatory frameworks”. They are called frameworks to stress their higher level of generality with respect to bare mechanisms; on the other hand, they are called explanatory because, as we show in this paper, their importance in explaining biological phenomena is not secondary with respect to mechanisms. We illustrate how explanatory frameworks establish selective and local criteria of causal relevance that drive the search for, characterisation and usage of biological mechanisms. Furthermore, we show that explanatory frameworks allow for changes of scientific perspective on the causal relevance of mechanisms going beyond the account provided by the new mechanistic philosophy

Some patients tolerate a given drug well, without adverse reactions. For others, though, an identical dose of the same medication can have toxic effects. Moreover, while a drug can be effective at relieving symptoms for some patients, it may fail to do the same for others suffering with the same disease. With such variability in treatment responses, tailoring medical interventions to individual patients has long been an aspiration of medicine. Until recently, however, medicine lacked a clear understanding of the biological reasons for human variation in drug response. Attempting to adjust treatment to the individual patient, physicians have relied primarily on direct observation, trial-and-error, and, ultimately...

This paper explores the epistemology of extrapolation from model organisms to humans in molecular medicine. We take into account two common views on the issue, the homology view and the disanalogy view. In response to both interpretations, we argue that the foundational basis of extrapolations cannot simply be provided by homology and that relevant disanalogies can, thanks to the techniques of molecular biology, be experimentally controlled and exploited to allow useful and reliable extrapolations. The case of "humanised mice" in the context of cancer stem cell research provides evidence of how animal models can be construed to approximate bona fide causal analogue models of human diseases. To supplement this view we show how the epistemology of model organisms needs to take into account the engineering side of molecular medicine. Model organisms are often manipulated to create analogies or remove disanalogies with the target system. We maintain that highlighting this feature is fundamental to explain what warrants extrapolation in the search for the molecular causes of disease

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact unless oversight activities by different stakeholders acquire a common substantive orientation.