S1340
Interdisciplinary - Education in radiation oncology
ESTRO 206
practice. Descriptive statistics were used for analysis. Results: A total of 149 residents participated (France: n = 131; North America: n = 18). Overall, 71% (n = 106) reported using one or more AI-based tools regularly or very often in clinical practice, and 95% (n = 141) had worked with departments employing AI-based organ-at-risk auto-contouring.Large language models were used differently across regions: French residents primarily for presentation preparation (57% n = 46) and scientific writing (55% n = 44), whereas North American residents used them mainly for literature searching (78% n = 14) and summarizing clinical or scientific content (67% n = 12).Despite frequent exposure, understanding of AI functioning was limited, with 68% (n = 89) of French residents and 61% (n = 11) of North American residents reporting moderate to very low confidence in their technical knowledge. Furthermore, 90% (n = 139) had not received formal AI training, while 86% (n = 132) expressed interest in structured educational programs.Ethical concerns varied between regions: French residents most commonly expressed fears of reduced radio-anatomy learning due to automated contouring (52% n = 97), while North American residents were primarily concerned with patient data security (61% n = 11). Nevertheless, overall acceptance of AI was high (78% n = 116), and 56% (n = 83) reported no change in patient interaction time. Conclusion: This international survey shows high AI usage and positive acceptance among radiation oncology residents in France and North America, with a clear need for education. Regional differences suggest that AI training should be adapted to local and institutional contexts to ensure meaningful integration. While these findings provide valuable intercontinental insight, North American data are preliminary and further responses are expected. These results support the development of standardized, ethically grounded AI training programs within radiation oncology curricula. Keywords: Artifical intelligence, education, radiotherapy Impact of AI-Avatar Based Digital Patient Engagement on Treatment Understanding, Empowerment and Stress in a US Community Radiation Oncology Practice Adam Raben, Shukla Gaurav, Naveroze Eduljee, Lindsey Romak, Adam Katzenberg, Kegelman Tim, Strasser Jon Radiation Oncology, Helen F Graham Cancer Center, Christiana Care Health System, Newark, USA Proffered Paper 4187
< 0.001). Knowledge of hygiene and preventive measures rose from 30–50 % → 80–95 %.Patients rated the tools as clear, accessible, and reassuring. Over 85 % valued the visual format of the brochure, and 80 % appreciated the website’s availability outside
clinical settings. The intervention significantly enhanced confidence and autonomy in self-
preparation. Conclusion:
The combination of printed, digital, and interactive educational tools significantly improved patient knowledge of preparation and side-effect prevention in pelvic radiotherapy. This multimodal, patient- centred approach offers an effective, reproducible model to strengthen adherence and treatment quality. Keywords: Patient education, Pelvic radiotherapy Digital Poster Highlight 4102 Perceptions of AI in Radiation Oncology Residency: A Bicontinental Survey by SFjRO and ROECSG Charles Raynaud 1,2 , Leah Katz 3 , Audrey Larnaudie 4,2 , Kevin L. Du 5 , Lucie Houdou 1,2 , Florence Huguet 6 , Malik Nebbache 7,2 , Rohan Patel 8 , Véronique Vendrely 9 , Jean- Emmanuel Bibault 10 1 Radiotherapy, Institut Gustave Roussy, Villejuif, France. 2 Société Française des jeunes Radiothérapeutes Oncologues, SFjRO, Paris, France. 3 Radiotherapy, University of Miami, Miami, USA. 4 Radiotherapy, Centre François Baclesse, Caen, France. 5 Radiotherapy, Yale University, New Haven, USA. 6 Radiotherapy, Tenon Hospital, Paris, France. 7 Radiotherapy, University Hospital of Brest, Brest, France. 8 Radiotherapy, University Hospitals Seidman Cancer Center, Cleveland, USA. 9 Radiotherapy, Centre Hospitalier et Universitaire de Bordeaux, Pessac, France. 10 Radiotherapy, Georges Pompidou European Hospital, Paris, France Purpose/Objective: Artificial intelligence (AI) is increasingly integrated into radiation oncology. This study aimed to evaluate the perceptions, expectations, and concerns of radiation oncology residents in France and North America, within the framework of a collaboration between the French Society of Young Radiation Oncologists (SFJRO) and the Radiation Oncology Education Collaborative Study Group (ROECSG). Material/Methods: A cross-sectional online survey was distributed to radiation oncology residents in France (March–April 2024) and in the United States and Canada (from September 2025 onward). The questionnaire included 21 items addressing exposure to AI-based tools, perceived level of understanding, training needs, ethical considerations, and acceptance of AI in clinical
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