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. 2024 Jan;34(1):348-354.
doi: 10.1007/s00330-023-09991-5. Epub 2023 Jul 29.

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022

Kicky G van Leeuwen  1 , Maarten de Rooij  2 , Steven Schalekamp  2 , Bram van Ginneken  2 , Matthieu J C M Rutten  2   3
Affiliations

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022

Kicky G van Leeuwen et al. Eur Radiol. 2024 Jan.

Abstract

Objectives: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.

Materials and methods: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis.

Results: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings.

Conclusion: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties.

Clinical relevance statement: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products.

Key points: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

Keywords: Artificial intelligence; Delivery of health care; Medical informatics; Radiology; Software.

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Conflict of interest statement

SS, MJCMR, and MdR declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

BvG is a co-founder of and receives royalties from Thirona and receives royalties from Delft Imaging and Mevis Medical Solutions. KvL has previously received speaker fees from Siemens Healthineers and contextflow. These disclosures are unrelated to the submitted work.

Figures

Fig. 1
Fig. 1
Clinical use of commercial AI in radiology departments in the Netherlands. a Hospital organizations using AI in their radiology department. b The total number of AI implementations. c The number of unique AI products being deployed in the Netherlands
Fig. 2
Fig. 2
Commercial AI products used in radiology departments in the Netherlands according to survey results from spring 2022. Product names are provided with the company name in italic. The number represents the number of implementations. If no number was given there was one implementation
Fig. 3
Fig. 3
Self-reported value of AI products in use by the respondent’s departments in 2022. Answers are provided by the representatives of the centers using AI in 2022
Fig. 4
Fig. 4
Main obstacles experienced for the purchase, validation, and implementation of AI tools in clinical practice as responded in 2022. Bars represent the frequency of the item mentioned by respondents. Multiple items per respondent were possible
Fig. 5
Fig. 5
Budget reserved for purchasing AI software in 2021 (a) and 2022 (b)
See this image and copyright information in PMC

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