Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around (...) cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

Digital Health Technologies (DHTs) are currently the subject of much debate both in terms of their technological frontiers as well as their ethical, legal and societal implications (ELSI). Regulation of such technologies as medical devices currently lacks behind their level of adoption. Digital Twins are the next evolution step of such DHTs and provide an opportunity to anticipate and act on ELSI before adoption again leaps before the necessary review. This paper introduces the concept and use cases of digital twins (...) in medicine, then frames the debate through the lens of related technologies, machine learning and personalized medicine, and maps ethical challenges stemming from those. Finally, we lay out how digital twins may change and challenge the future practice of medicine. (shrink)