Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

Health professionals increasingly face patients with complex health problems and this pressurizes them to cooperate. The authors have analyzed how the complexity of health care problems relates to two types of cooperation: consultation and multidisciplinary teamwork (MTW). Moreover, they have analyzed the impact of these two types of cooperation on perceived professional autonomy. Two teams were studied, one team dealing with geriatric patients and another treating oncology patients. The authors conducted semi-structured interviews, studied written documents, held informal discussions and observed (...) the teams at work. Consultation was most likely to take place when a patient had multiple problems. However, if these problems were interrelated, i.e. the solution for one problem interfered with solving another, then MTW was favored. The same was true when the available information was equivocal such that there were conflicting interpretations of a problem. How the professionals perceived the relationship between complexity and the need to cooperate depended on their expertise, their occupational background, and their work orientation. Consultation did not affect the professional autonomy of the health care professionals. MTW however did decrease the perceived level of professional autonomy. The extent to which this occurred seemed to depend on the quality of the interpersonal relations within the team. The findings can help in selecting the most appropriate and efficient type of cooperation based on the complexity of a patient’s problems. They can also help team leaders to stimulate reflection and feedback processes, and medical trainers to develop competencies among students related to such teamwork behaviors. (shrink)

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Contemporary epistemology of peer disagreement has largely focused on our immediate normative response to prima facie instances of disagreement. Whereas some philosophers demand that we should withhold judgment (or moderate our credences) in such cases, others argue that, unless new evidence becomes available, disagreement at best gives us reason to demote our interlocutor from his peer status. But what makes someone an epistemic peer in the first place? This question has not received the attention it deserves. I begin by surveying (...) different notions of ‘epistemic peer’ that have been peddled in the contemporary literature, arguing that they tend to build normative assumptions about the correct response to disagreement into the notion of peerhood. Instead, I argue, epistemic peerhood needs to be taken seriously in its own right. Importantly, for epistemic agents to count as peers, they should exhibit a comparable degree of reflective awareness of the character and limitations of their own knowledge. (shrink)