A holistic approach to implementing artificial intelligence in radiology
Abstract Objective Despite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI im...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01586-4 |
| _version_ | 1797276496051568640 |
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| author | Bomi Kim Stephan Romeijn Mark van Buchem Mohammad Hosein Rezazade Mehrizi Willem Grootjans |
| author_facet | Bomi Kim Stephan Romeijn Mark van Buchem Mohammad Hosein Rezazade Mehrizi Willem Grootjans |
| author_sort | Bomi Kim |
| collection | DOAJ |
| description | Abstract Objective Despite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach. Materials and methods We conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges. Results This study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice. Conclusion This study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice. Critical relevance statement Adoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice. Key points 1. Practical and actionable insights into successful AI implementation in radiology are lacking. 2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation 3. Holistic approach aids organisations to create sustainable value through AI implementation. Graphical Abstract |
| first_indexed | 2024-03-07T15:29:01Z |
| format | Article |
| id | doaj.art-b51d698715494045a5ab7cf9356b47f2 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-07T15:29:01Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-b51d698715494045a5ab7cf9356b47f22024-03-05T16:32:06ZengSpringerOpenInsights into Imaging1869-41012024-01-0115111010.1186/s13244-023-01586-4A holistic approach to implementing artificial intelligence in radiologyBomi Kim0Stephan Romeijn1Mark van Buchem2Mohammad Hosein Rezazade Mehrizi3Willem Grootjans4House of Innovation (Department of Entrepreneurship, Innovation and Technology), Stockholm School of EconomicsRadiology, Leiden University Medical CenterRadiology, Leiden University Medical CenterKIN Center for Digital Innovation, Vrije Universiteit AmsterdamRadiology, Leiden University Medical CenterAbstract Objective Despite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach. Materials and methods We conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges. Results This study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice. Conclusion This study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice. Critical relevance statement Adoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice. Key points 1. Practical and actionable insights into successful AI implementation in radiology are lacking. 2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation 3. Holistic approach aids organisations to create sustainable value through AI implementation. Graphical Abstracthttps://doi.org/10.1186/s13244-023-01586-4Artificial intelligenceImplementation scienceChange managementInformation systemsDigital technology |
| spellingShingle | Bomi Kim Stephan Romeijn Mark van Buchem Mohammad Hosein Rezazade Mehrizi Willem Grootjans A holistic approach to implementing artificial intelligence in radiology Insights into Imaging Artificial intelligence Implementation science Change management Information systems Digital technology |
| title | A holistic approach to implementing artificial intelligence in radiology |
| title_full | A holistic approach to implementing artificial intelligence in radiology |
| title_fullStr | A holistic approach to implementing artificial intelligence in radiology |
| title_full_unstemmed | A holistic approach to implementing artificial intelligence in radiology |
| title_short | A holistic approach to implementing artificial intelligence in radiology |
| title_sort | holistic approach to implementing artificial intelligence in radiology |
| topic | Artificial intelligence Implementation science Change management Information systems Digital technology |
| url | https://doi.org/10.1186/s13244-023-01586-4 |
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