ArticleJCO clinical cancer informatics2024
Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?
Article in JCO clinical cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Operationalizing the EU AI Act in a comprehensive cancer center through an institutional governance framework.NPJ digital medicine · 2026Review
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Artificial intelligence in implementation research: a scoping review of applications and recommendations.Translational behavioral medicine · 2026Article
- The anatomy of AI implementation skepticism in Polish healthcare: an explanatory mixed-methods analysis of psychographic barriers among healthcare professionals.Frontiers in public health · 2026Article
- Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study.JMIR research protocols · 2025Article
- Toward a human-centric co-design methodology for AI detection of differences between planned and delivered dose in radiotherapy.Journal of applied clinical medical physics · 2025Article
- Balancing between A and I.Acta oncologica (Stockholm, Sweden) · 2025Article
- Postoperative testicular metastasis in early to mid-stage gastric adenocarcinoma: a case report and literature review.Frontiers in oncology · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI) applications in radiotherapy (RT) are expected to save time and improve quality, but implementation remains limited. Therefore, we used implementation science to develop a format for designing an implementation strategy for AI. This study aimed to (1) apply this format to develop an AI implementation strategy for our center; (2) identify insights gained to enhance AI implementation using this format; and (3) assess the feasibility and acceptability of this format to design a center-specific implementation strategy for departments aiming to implement AI.
methodsWe created an AI-implementation strategy for our own center using implementation science methods. This included a stakeholder analysis, literature review, and interviews to identify facilitators and barriers, and designed strategies to overcome the barriers. These methods were subsequently used in a workshop with teams from seven Dutch RT centers to develop their own AI-implementation plans. The applicability, appropriateness, and feasibility were evaluated by the workshop participants, and relevant insights for AI implementation were summarized.
resultsThe stakeholder analysis identified internal (physicians, physicists, RT technicians, information technology, and education) and external (patients and representatives) stakeholders. Barriers and facilitators included concerns about opacity, privacy, data quality, legal aspects, knowledge, trust, stakeholder involvement, ethics, and multidisciplinary collaboration, all integrated into our implementation strategy. The workshop evaluation showed high acceptability (18 participants [90%]), appropriateness (17 participants [85%]), and feasibility (15 participants [75%]) of the implementation strategy. Sixteen participants fully agreed with the format.
conclusionOur study highlights the need for a collaborative approach to implement AI in RT. We designed a strategy to overcome organizational challenges, improve AI integration, and enhance patient care. Workshop feedback indicates the proposed methods are useful for multiple RT centers. Insights gained by applying the methods highlight the importance of multidisciplinary collaboration in the development and implementation of AI.
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