The uptake of artificial intelligence (AI) in health care is at an early stage. Recent studies have shown a lack of AI-specific implementation theories, models, or frameworks that could provide guidance for how to translate the potential of AI into daily health care practices. For AI to be successfully introduced to change clinical practice, we need to understand current practices and the contexts in which those practices are conducted, as well as how AI would fit with or change those ongoing practices and processes. However, the experiences of the professionals and patients who use a particular AI application are often overlooked. Thus, coproduction has been emphasized as a key factor for successful implementation of innovations such as AI in health care in order to develop effective strategies and to ensure that value is created. In July 2021, we initiated an eight-year research program at Halmstad University with funding from the Knowledge foundation, to conduct multidisciplinary research in co-production with researchers, healthcare professionals, patients, and industry partners in order to address this knowledge gap of implementation of AI in healthcare practice.

The research program is part of a regional and national initiative to build infrastructure to support the implementation of AI into practice. It builds on multidisciplinary collaboration between academics with expertise in data analytics, digital service, health economics, health care implementation, and health management as well as national and international collaboration with academic partners with expertise in healthcare improvement. The program is interdisciplinary and combines applied and theoretical approaches, using both qualitative and quantitative methods, and is built up through extensive collaboration with users, regions, municipalities, and industry.

The specific objectives of the research program is 1) to develop a theoretically informed framework for AI implementation in health care that can be applied to facilitate such implementation in routine health care practice, 2) to carry out empirical AI implementation studies, guided by the framework for AI implementation, and to generate learning for enhanced knowledge and operational insights to guide further refinement of the framework, 3) to apply the developed framework in clinical practice in order to develop regional capacity to provide the practical resources, competencies, and organizational structure required for AI implementation. © The Authors.