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.

Small‐ and medium‐sized enterprises (SMEs) largely depend on proficient idea generation activities to improve their front‐end innovation performance, yet the liabilities of newness and smallness often hamper SMEs' ability to benefit from systematic idea generation. To compensate for these liabilities, many SMEs adopt an open innovation approach by collaborating with market‐based partners such as customers and suppliers. This study investigates the relationship between SMEs' systematic idea generation and front‐end performance and investigates the moderating role of market‐based partnership for SMEs. Drawing on a survey of 146 Swedish manufacturing SMEs, this study provides two key contributions. First, the systematic idea generation and front‐end performance relationship in SMEs is non‐linear. Accordingly, higher levels of front‐end performance are achieved when idea generation activities are highly systematic. Second, the returns from higher levels of systematic idea generation are positively moderated by market‐based partnerships. Thus, external cooperation with customers and suppliers pays off most toward front‐end performance when SMEs have highly systematic idea generation processes. These results indicate a contingency perspective on the role of external partnerships. They also have implications for research into the front‐end of innovation and open innovation in the context of SMEs.

Firms are increasingly relying on collaborating with external partners to drive technology development. Many firms struggle with managing the inherently uncertain and ambiguous technology development process, especially with external actors involved, because they may not have or share the same project management practices concerning coordination and control activities. To address this gap, this study examines appropriate project management practices for market‐based and science‐based partnerships in three large technology‐intensive firms. Our results suggest that interorganizational technology development is problematic because firms lack sufficient partner understanding and struggle with aligning their project management practices with those of their partners. To address these problems, we identify project management practices of coordination and control to fit the contingencies of each type of partner collaboration. Our results provide implications for theory and managerial practices related to managing interorganizational technology development.

No abstract