Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Abstract

Background Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice. Objective This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice. Methods A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies. Results In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation. Conclusions This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science.

Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden

“…The conclusion in newly published reviews addressing regulation, privacy and legal aspects [ 15 , 16 ], ethics [ 16 – 18 ], clinical and patient outcomes [ 19 – 21 ] and economic impact [ 22 ], is that further research is needed in a real-world clinical setting although the clinical implementation of AI technology is still at an early stage. There are no studies describing implementation frameworks or models that could inform us concerning the role of barriers and facilitators in the implementation process and relevant implementation strategies of AI technology [ 23 ]. This illustrates a significant knowledge gap on how to implement AI in healthcare practice and how to understand the variation of acceptance of this technology among healthcare leaders, healthcare professionals, and patients [ 14 ].…”

“…We thus do not know if these models are applicable to AI as a socio-technical system or if other determinants are important for the implementation process. Likewise, based on a new literature study, we found no AI-specific implementation theories, frameworks, or models that could provide guidance for how leaders could facilitate the implementation and realize the potential of AI in healthcare [ 23 ]. We thus need to understand what the unique challenges are when implementing AI in healthcare practices.…”

Examining how Ethics in Relation to Health Technology is Described in the Research Literature: Scoping Review

“…This could reflect a tendency to raise more ethical concerns regarding implications within these technology areas instead of genetics and gene technology, gene-based screening, and technologies used for assistance during illness and rehabilitation. It could also reflect the current hype of applying health technologies based on health care data and artificial intelligence and that these technologies are more studied in general and with low reference to actual application and empirical grounding [ 28 ].…”

Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program

“…A few empirical studies with robust methodology, such as randomized controlled studies, have investigated the effects of implementation of AI technology in practice [20], but there are no AI-specific implementation theories, models, or frameworks that could provide guidance for how to translate the potential of AI into daily health care practices [22]. Thus, there is currently a paucity of knowledge concerning several key issues, including barriers and facilitators to successful implementation of AI in health care; what strategies might be used to support AI implementation; how the use of AI might change existing clinical workflows, roles, and responsibilities; or how the infrastructure of management and governance should be constructed to be effective [23].…”