Allocating access to unapproved COVID-19 drugs available via Pre-Approval Access pathways or Emergency Use Authorization raises unique challenges at the intersection of clinical care and research....

Deciding whether to grant an expanded access request for a child whose sibling is enrolled in a gene therapy trial involves a number of complex factors: considering the best interests of the child, the psychosocial and economic impact on the family, and the concerns and obligations of researchers. Despite the challenges in coming to a substantively fair outcome in cases of discordant eligibility, creating a procedurally fair decision-making process to adjudicate requests is essential.

The literature on the ethics of machine learning in healthcare contains a great deal of work on algorithmic fairness. But a focus on fairness has not been matched with sufficient attention to the relationship between machine learning and distributive justice in healthcare. A significant number of clinical prediction models have been developed which could be used to inform the allocation of scarce healthcare resources. As such, philosophical theories of distributive justice are relevant when considering the ethics of their design and (...) implementation. This paper considers the relationship between machine learning in healthcare and distributive justice with a focus on four aspects of algorithmic design and deployment: the choice of target variable, the model's socio-technical context, the choice of input variables, and the membership of the datasets that models are trained and validated on. Procedural recommendations for how these considerations should be accounted for in the design and implementation of such models follow. (shrink)

This paper considers the ethics of placebo‐controlled trials in developing countries, where a treatment already exists but is not available due to the low local standard of care. Such trials would not be permitted in more developed nations where a higher standard of care is available. I argue that there are moral intuitions against such trials, but a further intuition that if the trials were aimed at producing treatment options for the developing world, that would be more permissible than if (...) the trials were designed with the benefit of rich world people in mind. An approach based upon GA Cohen's work on interpersonal justifiability is suggested to allow us to explain these intuitions. Cohen's framework shows that these trials are ethically problematic because the inequality in healthcare provision between developing and developed nations that allows them to take place is at least partly the pharmaceutical corporations’ fault. Following Cohen's argument, this means the trials are non‐comprehensively justified. This allows for a more complete explanation of our intuitions than to consider such trials as cases of exploitation, because intuitions on the ethicacy of research can vary even when the exploitation relation remains the same. It is then established that there is good empirical evidence to believe that pharmaceutical corporations do fail the interpersonal justifiability test. The policy implications of this judgement are then considered, and it is suggested that the framework might be equally applicable to examining the permissibility of research conducted on vulnerable people within more developed nations. (shrink)

The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of black box machine learning (...) into resource allocation. It will then consider the arguments for informing patients about the use of machine learning in resource allocation, before exploring the challenge of whether individual patients could principally contest algorithmic prioritization decisions involving black box machine learning. Finally, this paper will examine how different forms of opacity in machine learning involved in resource allocation could be a barrier to patient consent to clinical decision-making in different healthcare contexts. (shrink)

The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of black box machine learning (...) into resource allocation. It will then consider the arguments for informing patients about the use of machine learning in resource allocation, before exploring the challenge of whether individual patients could principally contest algorithmic prioritization decisions involving black box machine learning. Finally, this paper will examine how different forms of opacity in machine learning involved in resource allocation could be a barrier to patient consent to clinical decision-making in different healthcare contexts. (shrink)