What question did this study set out to answer?

The research aims to establish a national framework for undergraduate radiation therapy education that incorporates artificial intelligence guidance and aligns with competency requirements.

July 24, 2026Strahlentherapie und Onkologie0 citationsOpen Access

DEGRO consensus framework for undergraduate radiation therapy teaching in Germany: a white paper with integrated guidance on artificial intelligence in medical education

PLPhilipp Linde BSBiney Pal Singh MNMaria Neu

Key Points

The research aims to establish a national framework for undergraduate radiation therapy education that incorporates artificial intelligence guidance and aligns with competency requirements.
Developed by the DEGRO working group 'Medical Education' with expert panel input.
Involved a structured consensus process with representatives from 22 out of 36 university departments in Germany.
Created a framework comprising core competencies, implementation standards, and an AI guidance model.
Identified nine core competencies for undergraduate education and a staged assessment model connected to the Miller pyramid.
Established various implementation levels based on anonymized site profiles.
Highlighted the tumor board as an effective integrative teaching format for radiation therapy.

Abstract

BACKGROUND AND PURPOSE: Undergraduate medical education is increasingly competency-based, digital, and interprofessional in accordance with the National Competence-Based Learning Objectives Catalogue for Medicine (Nationaler Kompetenzbasierter Lernzielkatalog Medizin, NKLM). Against this background, a nationally relevant, consensus-based framework for undergraduate radiation therapy education in Germany was developed by an expert panel of faculty representatives, aligned with the competencies expected at the end of the practical year (PJ) and in the final state examination context (M3), and complemented by guidance on the responsible educational use of artificial intelligence (AI). METHODS: The framework was prepared by the German Society of Radiation Oncology (Deutsche Gesellschaft für Radioonkologie, DEGRO) working group "Medical Education" (AG Lehre), including a consolidated master document and 16 teaching portfolios that informed 15 candidate competencies. In a second step, 25 formally delegated representatives from 22 of the 36 university radiation oncology departments in Germany participated in a structured expert consensus process incorporating individual prioritization, small-group refinement, plenary consolidation, predefined voting thresholds, and postworkshop editorial integration. RESULTS: The resulting framework comprised four components: (1) nine prioritized core competencies defining a national minimum standard at the PJ/M3 level; (2) implementation bandwidths (minimal, recommended, best practice) anchored in anonymized site-profile data; (3) a staged A/B/C assessment model aligned with the Miller pyramid; and (4) integrated AI guidance defining permitted use cases, nonnegotiable boundaries, AI literacy goals, and a practical educator-facing implementation logic. The tumor board was identified as a particularly suitable integrative teaching format. CONCLUSION: This white paper presents a nationally coordinated, competency-oriented framework for undergraduate radiation therapy education that combines a stable core with scalable local implementation options. By linking essential competencies, teaching bandwidths, staged assessment, and responsible AI guidance, greater consistency, coherence, and practical implementation of radiation therapy teaching across heterogeneous faculties is facilitated.

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