Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Abstract

Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study

“…Individuals with technological expertise or background showed a more positive attitude and expected AI applications to mature with time. The identified enablers align well with those found for AI applications in radiology, for instance the work in [ 43 ], where the authors interviewed radiologists, healthcare managers and industry stakeholders. Some important enablers were, as in the present study: (i) improved diagnostic quality and avoidance of mistakes; (ii) reduced workload by time-saving; (iii) more consistent reporting; (iv) having the AI integrated in existing routines and clinical pathways; and (v) openness towards AI applications [ 43 , 44 ].…”

“…Some medical specialist such as radiologists, who do not directly communicate with patients (requiring empathy which machines do not have), do not contrast their findings against clinical signs and symptoms (which is standard in dental care to come to diagnosis and therapy decision) and do not perform the subsequent therapy, are expected to undergo significant changes in their daily work induced by AI [ 42 ]. Fewer changes by AI implementation are likely for other physicians, dentists among them [ 1 , 43 , 44 , 45 , 46 ]. Notably, dental radiologists (a specialty which does not exist in Germany) may have brought up other arguments than the practitioners interviewed, something to highlight at this point.…”

Radiologists in the loop: the roles of radiologists in the development of AI applications

“…In addition, having resources available for hiring radiologists and systematically engaging them in the development is a key factor. Especially companies 2 As stated by the R&D manager of a small established AI vendor in the Netherlands: "we have, of course, ran into problems when developing. Not that we were completely wrong, but, it could have been done better if we used the user feedback from the beginning.…”

“…The AI applications developed in the radiology domain are growing rapidly [1]. Besides the challenges [2], these applications increasingly enter the clinical practice and can significantly impact the radiology work [3]. In this situation, the radiologists' role does not limit to being only the "users" of the applications.…”

“…The literature on AI development in general [5], and in the domain of radiology in particular, has proposed various roles that radiologists can and should play during the development of AI applications. As domain experts, they have the tacit knowledge, which can lead the AI developers towards medically relevant use cases [2,6]. They can advise developers on how their tools can effectively contribute to the radiology work.…”