Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?

Journal: JCO clinical cancer informatics
PMID: 39705640

Abstract

PURPOSE: Artificial 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.

Authors

  • Rachelle Swart
    Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
  • Liesbeth Boersma
    Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
  • Rianne Fijten
    Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
  • Wouter van Elmpt
    Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl.
  • Paul Cremers
    Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
  • Maria J G Jacobs
    Tilburg School of Economics and Management, Department of Management, Tilburg University, Tilburg, The Netherlands.

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