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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...

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Autores principales: Strohm, Lea, Hehakaya, Charisma, Ranschaert, Erik R., Boon, Wouter P. C., Moors, Ellen H. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476917/
https://www.ncbi.nlm.nih.gov/pubmed/32458173
http://dx.doi.org/10.1007/s00330-020-06946-y
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author Strohm, Lea
Hehakaya, Charisma
Ranschaert, Erik R.
Boon, Wouter P. C.
Moors, Ellen H. M.
author_facet Strohm, Lea
Hehakaya, Charisma
Ranschaert, Erik R.
Boon, Wouter P. C.
Moors, Ellen H. M.
author_sort Strohm, Lea
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-74769172020-09-21 Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors Strohm, Lea Hehakaya, Charisma Ranschaert, Erik R. Boon, Wouter P. C. Moors, Ellen H. M. Eur Radiol Imaging Informatics and Artificial Intelligence 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-05-26 2020 /pmc/articles/PMC7476917/ /pubmed/32458173 http://dx.doi.org/10.1007/s00330-020-06946-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Imaging Informatics and Artificial Intelligence
Strohm, Lea
Hehakaya, Charisma
Ranschaert, Erik R.
Boon, Wouter P. C.
Moors, Ellen H. M.
Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title_full Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title_fullStr Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title_full_unstemmed Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title_short Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
title_sort implementation of artificial intelligence (ai) applications in radiology: hindering and facilitating factors
topic Imaging Informatics and Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476917/
https://www.ncbi.nlm.nih.gov/pubmed/32458173
http://dx.doi.org/10.1007/s00330-020-06946-y
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