Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to potential changes of responsibility and decision-making (...) authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees’ reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians’ responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS. (shrink)