When is it justified to use opaque artificial intelligence (AI) output in medical decision-making? Consideration of this question is of central importance for the responsible use of opaque machine learning (ML) models, which have been shown to produce accurate and reliable diagnoses, prognoses, and treatment suggestions in medicine. In this article, I discuss the merits of two answers to the question. According to the Explanation View, clinicians must have access to an explanation of why an output was produced. According to the Validation View, it is sufficient that the AI system has been validated using established standards for safety and reliability. I defend the Explanation View against two lines of criticism, and I argue that within the framework of evidence-based medicine mere validation seems insufficient for the use of AI output. I end by characterizing the epistemic responsibility of clinicians and point out how a mere AI output cannot in itself ground a practical conclusion about what to do.

Recent research offers good reason to think that various psychedelic drugs—including psilocybin, ayahuasca, ketamine, MDMA, and LSD—may have significant therapeutic potential in the treatment of various mental health conditions, including post-traumatic stress disorder, depression, existential distress, and addiction. Although the use of psychoactive drugs, such as Diazepam or Ritalin, is well established, psychedelics arguably represent a therapeutic step change. As experiential therapies, their value would seem to lie in the subjective experiences they induce. As it is the only way for trainee psychedelic therapists to fully understand their subjective effects, some have suggested that firsthand experience of psychedelics should form part of training programs. We question this notion. First, we consider whether the epistemic benefits offered by drug-induced psychedelic experience are as unique as is supposed. We then reflect on the value it might have in regard to the training of psychedelic therapists. We conclude that, absent stronger evidence of the contribution drug-induced experiences make to the training of psychedelic therapists, requiring trainees to take psychedelic drugs does not seem ethically legitimate. However, given the potential for epistemic benefit cannot be entirely ruled out, permitting trainees who wish to gain first-hand experience of psychedelics may be permissible.

Intelligence, as measured by grades and/or standardized test scores, plays a principal role in the medical school admissions process in most nations. Yet while sufficient intelligence is necessary to practice medicine effectively, no evidence suggests that surplus intelligence beyond that threshold is correlated with providing higher quality medical care. This paper argues that using perceived measures of intelligence to distinguish between applicants, at levels that exceed the level of intelligence required to practice medicine, is both unfair to applicants and fails to serve the interests of patients.

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this article, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients.

The public debate on voluntary termination of life by elderly people, which has been an intensely controversial subject in the Netherlands for some time, has centered around the issue of “completed life” in recent years. In 2016, an ad hoc governmental advisory committee concluded that the already existing Euthanasia Act provided sufficient scope to resolve most of the problems related to the issue. Most of the older adults who feel they no longer have anything to look forward to in their lives and who have developed a wish to die as a result would be able to invoke this Act. Partly for this reason, the committee considered broadening the legal options relating to assisted suicide undesirable. Analysis of the assessment practice of the regional euthanasia review committees reveals that the room for interpretation offered by the Euthanasia Act is indeed considerable.