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Objectives Artificial intelligence (AI) is used in a variety of contexts in medicine. This involves the use of algorithms and software that analyze digital information to make diagnoses and suggest adapted therapies. It is unclear, however, what medical students know about AI in medicine, how they evaluate its application, and what they expect from their medical training accordingly. In the study presented here, we aimed at providing answers to these questions. Methods In this survey study, we asked medical students about their assessment of AI in medicine and recorded their ideas and suggestions for considering this topic in medical education. Fifty-eight medical students completed the survey. Results Almost all participants were aware of the use of AI in medicine and had an adequate understanding of it. They perceived AI in medicine to be reliable, trustworthy, and technically competent, but did not have much faith in it. They considered AI in medicine to be rather intelligent but not anthropomorphic. Participants were interested in the opportunities of AI in the medical context and wanted to learn more about it. They indicated that basic AI knowledge should be taught in medical studies, in particular, knowledge about modes of operation, ethics, areas of application, reliability, and possible risks. Conclusions We discuss the implications of these findings for the curricular development in medical education. Medical students need to be equipped with the knowledge and skills to use AI effectively and ethically in their future practice. This includes understanding the limitations and potential biases of AI algorithms by teaching the sensible use of human oversight and continuous monitoring to catch errors in AI algorithms and ensure that final decisions are made by human clinicians.

Objective Digital transformation in higher education has presented medical students with new challenges, which has increased the difficulty of organising their own studies. The main objective of this study is to evaluate the effectiveness of a chatbot in assessing the stress levels of medical students in everyday conversations and to identify the main condition for accepting a chatbot as a conversational partner based on validated stress instruments, such as the Perceived Stress Questionnaire (PSQ20). Methods In this mixed-methods research design, medical-student stress level was assessed using a quantitative (digital- and paper-based versions of PSQ20) and qualitative (chatbot conversation) study design. PSQ20 items were also shortened to investigate whether medical students’ stress levels can be measured in everyday conversations. Therefore, items were integrated into the chat between medical students and a chatbot named Melinda. Results PSQ20 revealed increased stress levels in 43.4% of medical students who participated ( N = 136). The integrated PSQ20 items in the conversations with Melinda obtained similar subjective stress degree results in the statistical analysis of both PSQ20 versions. Qualitative analysis revealed that certain functional and technical requirements have a significant impact on the expected use and success of the chatbot. Conclusion The results suggest that chatbots are promising as personal digital assistants for medical students; they can detect students’ stress factors during the conversation. Increasing the chatbot's technical and social capabilities could have a positive impact on user acceptance.