[Participatory approaches in the development of AI applications in medicine: opportunities and challenges]

The increasing integration of artificial intelligence (AI) in healthcare not only holds the potential for efficiency gains, personalized medicine, and evidence-based decisions but also raises ethical and social challenges, such as bias, lack of transparency, and acceptance. Participatory approaches that actively involve patients, physicians, caregivers, and other stakeholders in the development process make it possible to align technological innovations with actual needs and to design them in a socially just way.The analysis distinguishes between participation as active co-design and partaking as access to social resources. Theoretical models such as the "ladder of participation" (Arnstein) illustrate the different levels of participation. In addition, methodological approaches such as action research, community-based participatory research, ethics by design, and value-sensitive design are discussed, which promote early ethical reflection and continuous user feedback.Practical examples such as KIPA (AI-supported patient information), KIDELIR (delirium prevention in care), and PRIVETDIS (neurotechnologies and mental privacy) show how participatory research can contribute to the optimization of care concepts. In addition to opportunities such as increased acceptance and user-centered technology design, challenges are identified, including limited resources, lack of representativeness, and invisible additional burdens for those involved. Finally, it is emphasized that in addition to technical and regulatory measures, continuous ethical reflection and transparent communication are essential to implement trustworthy and effective AI systems in healthcare.