Until recently, moral case deliberation (MCD) sessions have mostly been facilitated by external experts, mainly professional ethicists. We have developed a train the facilitator programme for healthcare professionals aimed at providing them with the competences needed for being an MCD facilitator. In this paper, we present the first results of a study in which we evaluated the programme. We used a mixed methods design. One hundred and twenty trained healthcare professionals and five trainers from 16 training groups working in different (...) healthcare organisations throughout the Netherlands were included. After completion of the programme, participants feel sufficiently confident and equipped to facilitate an MCD session. Feeling competent does not mean that participants have no doubts or questions left. Rather, they are aware of their limitations and see the need for continuous learning. According to the respondents, the actual exercise of facilitating MCD during and in between the training sessions contributed most to the development of competences necessary for being an MCD facilitator. Respondents without prior experience of participating in MCD sessions felt less competent after the training than those who had participated in MCD sessions before. Self-attributed competence varied between participants with different professional backgrounds. (shrink)

This paper motivates institutional epistemic trust as an important ethical consideration informing the responsible development and implementation of artificial intelligence (AI) technologies (or AI-inclusivity) in healthcare. Drawing on recent literature on epistemic trust and public trust in science, we start by examining the conditions under which we can have institutional epistemic trust in AI-inclusive healthcare systems and their members as providers of medical information and advice. In particular, we discuss that institutional epistemic trust in AI-inclusive healthcare depends, in part, on (...) the reliability of AI-inclusive medical practices and programs, its knowledge and understanding among different stakeholders involved, its effect on epistemic and communicative duties and burdens on medical professionals and, finally, its interaction and alignment with the public’s ethical values and interests as well as background sociopolitical conditions against which AI-inclusive healthcare systems are embedded. To assess the applicability of these conditions, we explore a recent proposal for AI-inclusivity within the Dutch Newborn Screening Program. In doing so, we illustrate the importance, scope, and potential challenges of fostering and maintaining institutional epistemic trust in a context where generating, assessing, and providing reliable and timely screening results for genetic risk is of high priority. Finally, to motivate the general relevance of our discussion and case study, we end with suggestions for strategies, interventions, and measures for AI-inclusivity in healthcare more widely. (shrink)