aceAI-assisted organ-at-risk contouring in undergraduate radiation therapy training
Source article: Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program
Abstract: Introduction The integration of artificial intelligence (AI) tools into radiation therapy workflows offers significant opportunities to improve efficiency by automating tasks such as contouring organs at risk (OARs). However, this also raises concerns regarding future practitioners' ability to critically evaluate auto-generated contours. This educational perspective examines how OAR contouring education can be integrated into undergraduate radiation therapy programs to support the development of foundational con…
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This peer-reviewed educational perspective from August 2026 examined how organ-at-risk contouring is taught in an Australian undergraduate radiation therapy program as AI auto-contouring enters clinical workflows. The authors reviewed curriculum scope and technologies and examined students' preferred methods, confidence across OARs, and perceived factors affecting quality.
The work matters because automating contouring could improve workflow efficiency but also risks eroding foundational competence needed to catch AI errors in treatment planning. Uncertainty remains about how well single-institution confidence and preference findings transfer to other programs and whether scaffolded anatomy and evaluation training actually preserves safe, accurate contouring when AI tools are used.
- Curriculum analysis covered a wide range of OARs across anatomical regions and imaging modalities for progressive skill development.
- Students preferred guided contouring methods where pre-generated contours could be edited for efficiency.
- Self-rated confidence varied by structure, with lower confidence for smaller and anatomically complex OARs.
- Anatomy knowledge was identified as the primary factor influencing contouring accuracy.
AI tools integrated into radiation therapy workflows can improve efficiency by automating organ-at-risk contouring tasks.
AI auto-contouring raises concerns that future radiation therapy practitioners may have reduced ability to critically evaluate auto-generated contours.
The rundown
The authors analyzed their undergraduate radiation therapy OAR contouring curriculum and surveyed students on preferred methods, self-rated confidence across different OARs, and factors influencing contour quality.
Results showed progressive exposure across regions and modalities, a student preference for editing pre-generated contours, anatomy knowledge as the main accuracy driver, and lower confidence for small or complex structures, leading authors to propose a scaffolded approach emphasizing anatomy and evaluation skills.
Findings are based on a single undergraduate program, limiting generalizability beyond the authors' institution.
Sources
- Peer-reviewedJournal of Medical Imaging and Radiation Sciences2026-08-22
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