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

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.

Bridging the care gap: patients' needs and experiences regarding shared decision-making in radiotherapy.

Background And Purpose: Shared decision-making (SDM), a collaborative process in which patients and physicians jointly determine further treatment, has been associated with numerous positive effects. However, its implementation into routine clinical practice faces challenges. In radiotherapy (RT) it may have additional challenges, since patients are referred from another oncologist, often "to undergo RT".

Predictors of long-term success after high-density mapping-guided substrate ablation procedures for ventricular tachycardia in patients with ischemic cardiomyopathy.

Introduction: High-density (HD) substrate mapping may increase success of catheter ablation targeting ventricular tachycardia (VT). However, despite its use, recurrent VT is not uncommon. We aim to investigate factors that are associated with outcomes after HD mapping-guided substrate ablation procedures for VT in patients with ischemic cardiomyopathy.