S2819
RTT - RTT education, training, and advanced practice
ESTRO 2026
sessions. Participants described the simulation as realistic and valuable, particularly the opportunity to practice conversations safely. Deliberately making mistakes to test the avatar’s responses was noted as a beneficial learning strategy. The Deeptalk simulation shows how generative AI can facilitate natural conversation while maintaining control over feedback (figure) and scoring. Integrating digital simulations with actor-based training remain important to strengthen emotional and non-verbal aspects of care.
Purpose/Objective: Automation and artificial intelligence (AI) are increasingly transforming radiotherapy, expanding the technical role of radiation therapy technologists (RTTs). However, effective treatment also depends on how RTTs communicate with patients, demonstrate empathy and build trust. The SAFE Europe study, shows that patients’ trust in RTTs is influenced by clear communication, sensitivity, and respect for dignity (1). While AI increases efficiency, it cannot replace empathy or authentic human connection (2). Many RTTs recognize the importance of psychosocial care but report insufficient time and training to address patients’ emotional needs effectively (3).Traditional radiotherapy education provides limited opportunities for repeated communication practice and actor-based training is often constrained by budget. Simulation- based learning offers a safe, structured way to practice and receive feedback (4).This study explores how an AI-simulation practice tool enhances RTT communication learning. Material/Methods: An interactive, avatar-based simulation focused on addressing patient concerns and questions about radiation safety was developed using the Deeptalk module in DialogueTrainer®. The content was based on guidelines from radiation safety teachers regarding the ALARA principle and input from communication experts, using comfort talk, empathic listening, recognizing fear and responding to questions. The generative AI component enables real-time, two-way conversations with avatars, creating a realistic “real- life” experience (figure). Seven participants, including communication and radiation safety teachers, took part in a pilot study. Each simulation provided color- coded feedback and a brief reflection exercise. Data included session counts, scores and qualitative feedback.
Conclusion: Generative AI simulations, such as Deeptalk, offer an ethical, measurable and engaging approach to training RTT communication skills. While they cannot replace genuine empathy or human connection, they provide structured and safe opportunities to practice professional dialogue, helping RTTs preserve the human connection in an increasingly technology- driven field. Keywords: RTT-patient communication, AI-simulation training References: 1. Flood T, O’Neill A, Oliveira C et al. Patients’ perspectives of the skills and competencies of radiation therapists (TRs/RTTs) in the UK, Portugal and Malta; the SAFE Europe project. Radiography. 2023;29:1–11.2.Ghafourifard M, Ghasempour M, et al. The AI fever: can artificial intelligence replace compassionate human care? J Caring Science. 2025;14(2):135–7. 3. Bezanson S, Aas E, et al. Survey of radiation therapists’ current practices and perceptions of psychosocial and supportive care in Canada and Norway. Support Care Cancer. 2025;33(5):447.4. Gousseva M, Pluymaekers, M, et al. Creating authentic and effective practice scenarios for digital simulation- based conversation training. Research and Practice Techn.Enh.Learning. 2024;9,036.
Results: A total of 21 play sessions were completed, with an average duration of 8 minutes and a total playtime of 2 hours and 46 minutes. Scores ranged from 6% to 75%. Instructors who completed multiple sessions improved their scores by an average of 15.6%, with individual progress of +19% and +21% between
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