AI is believed to have the potential to radically change modern medicine. Medical AI systems are developed to improve diagnosis, prediction, and treatment of a wide array of medical conditions. It is assumed to enable more accurate and efficient ways to diagnose diseases and “to restore the precious and time-honored connection and trust – the human touch – between patients and doctors“ (Topol, 2019, p. 18), by enabling health care professionals to spend more time with their patients. Sophisticated self-learning AI systems that do not follow predetermined decision rules – often referred to as black-boxes (Esteva et al 2019; Shortliffe et al 2018) – have spawned philosophical debate: the black-box nature of AI systems is believed to be a major ethical challenge for the use of these systems in medicine and it remains disputed whether explainability is philosophically and computationally possible. This special issue focuses on the ethics and epistemology of explainability in medical AI broadly construed.@en

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence (AI). To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care.@en

Medical AI is increasingly being developed and tested to improve medical diagnosis, prediction and treatment of a wide array of medical conditions. Despite worries about the explainability and accuracy of such medical AI systems, it is reasonable to assume that they will be increasingly implemented in medical practice. Current ethical debates focus mainly on design requirements and suggest embedding certain values such as transparency, fairness, and explainability in the design of medical AI systems. Aside from concerns about their design, medical AI systems also raise questions with regard to physicians' responsibilities once these technologies are being implemented and used. How do physicians’ responsibilities change with the implementation of medical AI? Which set of competencies do physicians have to learn to responsibly interact with medical AI? In the present article, we will introduce the notion of forward-looking responsibility and enumerate through this conceptual lens a number of competencies and duties that physicians ought to employ to responsibly utilize medical AI in practice. Those include amongst others understanding the range of reasonable outputs, being aware of own experience and skill decline, and monitoring potential accuracy decline of the AI systems.@en

Scientific discoveries are often to some degree influenced by luck. Whether luck’s influence is at odds with common-sense intuitions about responsibility, is the central concern of the philosophical debate about moral luck. Do scientists acknowledge that luck plays a role in their work and–if so–do they consider it morally problematic? The present article discusses the results of four focus groups with scientists, who were asked about their views on luck in their fields and its moral implications. The participants underscored circumstantial luck as a key dimension of luck in science. Nevertheless, most participants insisted that there are ways of executing ‘control’ in science: They believe that virtues and skills can increase one’s chances for success. The cultivation of these skills and virtues was considered a reasonable ground for pride. Prizes and rewards were rarely tied to personal desert, but instead to their societal function.

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The world has been startled by the irresponsible experiment of He Jiankui, who used CRISPR to genetically modify human embryos. In this viewpoint, we explore the phenomenon of moral luck in medicine and its bearing on the limits of simple judgements of the kind “everything that ends well is well” or “someone broke the rules, and is therefore blameworthy”. The risks involved in scientific and medical experiments are often brushed aside, when they turn out well. The clinical application of CRISPR in the human germline is presently too risky to be used without more preclinical research and unacceptable without broader societal support, which justifies the call for a moratorium by the scientific community. However, such policies do not determine how to assess cases, where someone was willing to take such risks beyond all rules, guidelines and regulation and succeeds. The policies including the proposed moratorium are as unanimous about the undesirability of current applications of clinical germline editing as they are about the potential importance of this research. What if this potential is achieved by breaking the rules? The paradox of moral luck impinges on this debate. In our analysis, we rebut simplified judgments and advocate a more balanced view on the relation between moral responsibility and the societal consequences of medicine.

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Dementia is highly prevalent and up until now, still incurable. If we may believe the narrative that is currently dominant in dementia research, in the future we will not have to suffer from dementia anymore, as there will be a simple techno-fix solution. It is just a matter of time before we can solve the growing public health problem of dementia. In this paper we take a critical stance towards overly positive narratives of techno-fixes by placing our empirical analysis of dementia research protocols and political statements in a framework of technology assessment. From this perspective, it becomes obvious that a techno-fix is just one of many ways to approach societal problems and more importantly that technologies are way less perfect than they are presented. We will argue that this narrow scope, which focusses on the usual suspects for solving illnesses, reduces dementia to organismic aspects, and may be counterproductive in finding a cure for dementia. We conclude with outlining how the narrow scope can be balanced with other narratives and why we should have a reasonable scepticism towards the usual suspects.

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