This book sheds light on various ethical challenges military and humanitarian health care personnel face while working in adverse conditions. Contexts of armed conflict, hybrid wars or other forms of violence short of war, as well as natural disasters, all have in common that ordinary circumstances can no longer be taken for granted. Hence, the provision of health care has to adapt, for example, to a different level of risk, to scarce resources, or uncommon approaches due to external incentives or (...) requirements. This affects the practice of health care as well as its ethics. This book offers a panoramic overview on various challenges healthcare faces in extraordinary situations and provides new insights from practitioners’ as well as from academic scholars’ perspectives. (shrink)

Patient preference predictors (PPPs) promise to provide medical professionals with a new solution to the problem of making treatment decisions on behalf of incapacitated patients. I show that the use of PPPs faces a version of a normative problem familiar from legal scholarship: the problem of naked statistical evidence. I sketch two sorts of possible reply, vindicating and debunking, and suggest that our reply to the problem in the one domain ought to mirror our reply in the other. The conclusion (...) is thus conditional: if we think the problem of naked statistical evidence is a serious problem in the legal domain, then we should be concerned about the symmetrical problem for PPPs. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

Dementia is highly prevalent and incurable. The participation of dementia patients in clinical research is indispensable if we want to find an effective treatment for dementia. However, one of the primary challenges in dementia research is the patients’ gradual loss of the capacity to consent. Patients with dementia are characterized by the fact that, at an earlier stage of their life, they were able to give their consent to participation in research. Therefore, the phase when patients are still competent to (...) decide offers a valuable opportunity to authorize research, by using an advance research directive. Yet, the use of ARDs as an authorization for research participation remains controversial. In this paper we discuss the role of autonomous decision-making and the protection of incompetent research subjects. We will show why ARDs are a morally defensible basis for the inclusion of this population in biomedical research and that the use of ARDs is compatible with the protection of incompetent research subjects. (shrink)

BackgroundAdvance research directives (ARD) have been suggested as a means by which to facilitate research with incapacitated subjects, in particular in the context of dementia research. However, established disclosure requirements for study participation raise an ethical problem for the application of ARDs: While regular consent procedures call for detailed information on a specific study (“token disclosure”), ARDs can typically only include generic information (“type disclosure”). The introduction of ARDs could thus establish a double standard in the sense that within the (...) context of ARDs, type disclosure would be considered sufficient, while beyond this context, token disclosure would remain necessary.Main bodyThis paper provides an ethical analysis of ARDs, taking into account the results of numerous empirical studies that have been performed so far. It will be argued that a revised understanding of informed consent can allow for context-sensitive disclosure standards. As a consequence, ARDs that include type disclosure can be acceptable under suitable circumstances. Such an approach raises a number of objections. A thorough examination shows, however, that they are not sufficient to justify a rejection of the approach.ConclusionThe approach presented in this paper avoids introducing a double standard. It is, therefore, more suitable for the implementation of ARDs than established approaches. (shrink)

When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such (...) a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies. (shrink)