Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2026
The medical physicist's role in the artificial intelligence era: an updated EFOMP curricular and professional framework.
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Kagadis George C, Diaz Oliver, Meades Richard, Pauwels Ruben, Colgan Niall, Zanca Federica, Tzanis Eleftherios, Visvikis Dimitrios
Аннотация
PURPOSE: Five years after the European Federation of Organizations for Medical Physics (EFOMP) published its first curricular framework for artificial intelligence (AI) in medical physics, the updated core curricula for radiology, radiotherapy and nuclear medicine have incorporated AI as a core competency rather than an addendum, a change consistent with the framework's principal recommendation. This paper examines the resulting educational landscape and defines the next stage of AI competency for the profession.
MATERIALS AND METHODS: Focus Group 3 of the EFOMP Artificial Intelligence Special Interest Group used a structured, iterative process of expert deliberation combining comparative curriculum analysis, competency mapping, regulatory analysis and iterative expert deliberation within the Working Group. Four primary source documents were analyzed in depth: the 2021 EFOMP white paper and the three updated EFOMP subspecialty core curricula for radiology, radiotherapy and nuclear medicine. The AI-related content of the three curricula was systematically compared against a predefined analytical framework covering scope, depth of treatment, European Credit Transfer and Accumulation System (ECTS) allocation, terminology, regulatory references, and treatment of recent AI developments. Competency gaps beyond the entry-level curricula were identified and articulated in the knowledge-skills-competence format used in EFOMP curricula. The resulting framework was informed by three large-scale surveys of medical physicists conducted in 2020, 2024 and 2025, and underwent iterative review within the Working Group before consultation within the EFOMP community.
RESULTS: The three updated curricula converge on a shared methodological foundation but diverge in ECTS allocation, coverage of recent AI architectures, and treatment of the EU AI Act, and six competency gaps were identified as common to all three subspecialties. A cross-subspecialty advanced competency framework is proposed, organized into four domains: validation and quality assurance, AI lifecycle management, generative AI and foundation models, and governance. A professional lifecycle model is introduced describing how these competencies should be acquired during specialist training, maintained through accredited continuing professional development, and formally recognized throughout a medical physicist's career. The medical physicist's responsibilities under the EU AI Act are defined, and the proposed framework is translated into practical recommendations for EFOMP, its National Member Organizations, training centers, and individual practitioners.
CONCLUSIONS: The profession's challenge has evolved from demonstrating the importance of AI to ensuring its safe, trustworthy, effective and regulatory-compliant integration into clinical practice, thereby strengthening patient safety and quality of care.
MATERIALS AND METHODS: Focus Group 3 of the EFOMP Artificial Intelligence Special Interest Group used a structured, iterative process of expert deliberation combining comparative curriculum analysis, competency mapping, regulatory analysis and iterative expert deliberation within the Working Group. Four primary source documents were analyzed in depth: the 2021 EFOMP white paper and the three updated EFOMP subspecialty core curricula for radiology, radiotherapy and nuclear medicine. The AI-related content of the three curricula was systematically compared against a predefined analytical framework covering scope, depth of treatment, European Credit Transfer and Accumulation System (ECTS) allocation, terminology, regulatory references, and treatment of recent AI developments. Competency gaps beyond the entry-level curricula were identified and articulated in the knowledge-skills-competence format used in EFOMP curricula. The resulting framework was informed by three large-scale surveys of medical physicists conducted in 2020, 2024 and 2025, and underwent iterative review within the Working Group before consultation within the EFOMP community.
RESULTS: The three updated curricula converge on a shared methodological foundation but diverge in ECTS allocation, coverage of recent AI architectures, and treatment of the EU AI Act, and six competency gaps were identified as common to all three subspecialties. A cross-subspecialty advanced competency framework is proposed, organized into four domains: validation and quality assurance, AI lifecycle management, generative AI and foundation models, and governance. A professional lifecycle model is introduced describing how these competencies should be acquired during specialist training, maintained through accredited continuing professional development, and formally recognized throughout a medical physicist's career. The medical physicist's responsibilities under the EU AI Act are defined, and the proposed framework is translated into practical recommendations for EFOMP, its National Member Organizations, training centers, and individual practitioners.
CONCLUSIONS: The profession's challenge has evolved from demonstrating the importance of AI to ensuring its safe, trustworthy, effective and regulatory-compliant integration into clinical practice, thereby strengthening patient safety and quality of care.