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".

Contextual success and pitfalls of mHealth service for maternal and child health in Africa: An Intervention, Context, Actors, Mechanism, and Outcome (ICAMO) framework guided systematic review of qualitative evidence.

Introduction: Mobile health (mHealth) interventions have shown potential to improve maternal and child health outcomes in Africa, but their effectiveness depends on specific interventions, context, and implementation quality. Challenges such as limited infrastructure, low digital literacy, and sustainability need to be addressed. Further evaluation studies are essential to summarize the impact of mHealth interventions.

The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network.

Purpose: This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data.

Summary Of Background: For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life.

Towards a European prospective data registry for particle therapy.

The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers.