Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

IntroductionArtificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences.AimThe aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review.Utilizing knowledge from the four fieldsThe four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare.ConclusionKnowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare.

Background Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. Objective This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. Methods Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. Results Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. Conclusions Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

BACKGROUND Artificial intelligence (AI) has the potential in healthcare to transform patient care and administrative processes, yet healthcare has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge to guide such processes when implementing AI-based applications in healthcare. OBJECTIVE The aim of this paper is to provide a protocol for the development, testing and evaluation of a framework, AI-QIF (Artificial Intelligence-Quality Implementation Framework), intended to guide decisions and activities related to the implementation of various AI-based applications in healthcare. METHODS The article outlines the development of an AI implementation framework for healthcare in five phases based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and to plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope and detailed guidance concerning important activities and considerations for successful implementation. The development process starts with phase I, in which an AI-adapted version of QIF is created. Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to testing the prototype of the AI-QIF in a healthcare environment. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Co-creation is a guiding principle for the project, and collaboration between researchers and various stakeholders will take place to varying degrees in the five phases. The process will draw on research-based and practice-based knowledge. RESULTS The AI-QIF will draw on numerous knowledge sources. The framework will be under continuous development and refinement. Insights gained during this process will be used as the foundation for parallel investments in regional capacity to increase the practical resources, competencies and organizational structures required to facilitate implementation of AI-based applications. This work will be carried out in collaboration with representatives from academia, strategic partners from the business sector, as well as political and operational leaders and teams from the regional and municipal healthcare systems. CONCLUSIONS The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in healthcare based on the premise that implementation processes benefit from being well prepared and structured. The framework will be co-produced to enhance its relevance, validity, usefulness and potential value for application in practice. CLINICALTRIAL Not relevant.

BACKGROUND AI applications in healthcare are expected to provide value for health care organizations, professionals as well as patients. However, implementation of such systems should be carefully planned and organized in order to ensure quality, safety and acceptance. OBJECTIVE This study aimed to understand the context and stakeholder perspectives related to future implementation of a decision support system for readmission prediction of heart failure patients. METHODS Interviews were held with 12 stakeholders from the regional and municipal healthcare organizations to gather their views on what effects implementation of such decision support system could have. Data was analysed based on the categories of barriers and enablers defined in the NASSS framework. RESULTS Stakeholders had in general a positive attitude and curiosity towards AI-based decision support systems, and lifted several barriers and enablers based on experiences of previous implementations of IT-systems. Aspects brought up related to all categories in the NASSS framework. CONCLUSIONS Several ideas were put forward on how the proposed AI-system would potentially affect and provide value for patients, professionals and the organization, and implementation aspects were important parts of that. A successful system need not only technological and clinical precision, but also a carefully planned implementation process. Of importance for further planning was the placement of the application in the care process. CLINICALTRIAL Not applicable