Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.
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...
| Published in: | European Radiology Vol. 30; no. 10; pp. 5525 - 5533 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145536777&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145536777 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2020 vid: 30 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145536777 143914123 145536777 NLM32458173 145536777 10.1007/s00330-020-06946-y NLM32458173 145536777 ppf: 5525 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. aug: au: Strohm, Lea Hehakaya, Charisma Ranschaert, Erik R. Boon, Wouter P. C. Moors, Ellen H. M. affil: Innovation Studies, Copernicus Institute of Sustainable Development, University Utrecht, Utrecht, The Netherlands sug: subj: Artificial Intelligence Trends Specialties, Medical Trends Radiography Trends Program Evaluation Qualitative Studies Program Development Data Collection Netherlands Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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