[No abstract available]

Introduction: Artificial 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.

Aim: The 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 fields: The 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.

Conclusion: Knowledge 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. © 2022 Nilsen, Reed, Nair, Savage, Macrae, Barlow, Svedberg, Larsson, Lundgren and Nygren. 

Background: Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.

Challenges with the three components of EBP: Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.

AI assistance for the three components of EBP: AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.

Conclusion: This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare. 

©2024 The Authors

Background: The process of translation of AI and its potential benefits into practice in healthcare services has been slow in spite of its rapid development. Trust in AI in relation to implementation processes is an important aspect. Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities.

Objective: This study aimed to explore the scientific literature regarding how trust in AI in relation to implementation in healthcare is conceptualized and what influences trust in AI in relation to implementation in healthcare.

Methods: This scoping review included five scientific databases. These were searched to identify publications related to the study aims. Articles were included if they were published in English, after 2012, and peer-reviewed. Two independent reviewers conducted an abstract and full-text review, as well as carrying out a thematic analysis with an inductive approach to address the study aims. The review was reported in accordance with the PRISMA-ScR guidelines.

Results: A total of eight studies were included in the final review. We found that trust was conceptualized in different ways. Most empirical studies had an individual perspective where trust was directed toward the technology's capability. Two studies focused on trust as relational between people in the context of the AI application rather than as having trust in the technology itself. Trust was also understood by its determinants and as having a mediating role, positioned between characteristics and AI use. The thematic analysis yielded three themes: individual characteristics, AI characteristics and contextual characteristics, which influence trust in AI in relation to implementation in healthcare.

Conclusions: Findings showed that the conceptualization of trust in AI differed between the studies, as well as which determinants they accounted for as influencing trust. Few studies looked beyond individual characteristics and AI characteristics. Future empirical research addressing trust in AI in relation to implementation in healthcare should have a more holistic view of the concept to be able to manage the many challenges, uncertainties, and perceived risks.