Requirements and key factors for the successful implementation of AI in clinical practice: a qualitative study from the European CAIDX network

Introduction Artificial intelligence (AI) is expected to become increasingly important in hospitals and clinical care. However, successful implementation depends on technical, organizational, and user-related factors. We aimed to identify the key requirements for equitable and effective adoption of AI-based applications in clinical practice. Methods Within the “ Clinical AI-based Diagnostics” (CAIDX) project, we conducted systematic expert interviews with 55 stakeholders from four European countries working across the healthcare sector. Interviews explored the perceived added value of AI, barriers to implementation, usability requirements, and potential health-economic implications. Results Stakeholder analysis revealed several recurrent sources of tension affecting implementation: limited physician involvement, differing perspectives between IT specialists and clinicians, unclear distribution of tasks and responsibilities, and low institutional commitment driven by concerns about inefficient use of time and resources. We also identified structural and workforce-related constraints that make implementation particularly challenging, including the large number of actors involved and the highly individual organizational setup of hospitals, which limits transferability of implementation models. Discussion Implementation of clinical AI requires targeted action at hospital, vendor, and health-system levels. Compared with earlier healthcare technologies, AI adoption is shaped more strongly by AI literacy, professional attitudes, and perceptions of trust and responsibility. Based on these findings, we propose practical recommendations to support implementation of AI applications in routine care.