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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Expanding the medical physicist curricular and professional programme to include Artificial Intelligence.

F Zanca1, I Hernandez-Giron2, M Avanzo3

  • 1Palindromo Consulting, Leuven, Belgium.

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)
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PubMed
Summary

This guideline curriculum introduces Artificial Intelligence (AI) to European Medical Physicists (MPs), covering basic and advanced concepts for medical imaging and radiation therapy. It aims to enhance AI knowledge and skills within the medical physics community.

Keywords:
Artificial intelligenceContinuing professional development (CPD)EFOMPEducation and trainingMedical physicist

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Area of Science:

  • Medical Physics
  • Artificial Intelligence
  • Medical Imaging
  • Radiation Therapy

Background:

  • The integration of Artificial Intelligence (AI) into medical physics practice necessitates a structured educational framework.
  • Existing training programs may not adequately cover the specific needs of Medical Physicists (MPs) in AI-related fields.
  • A standardized curriculum is required to ensure consistent and effective AI education for European MPs.

Purpose of the Study:

  • To establish a comprehensive guideline curriculum for Artificial Intelligence (AI) education and training tailored to European Medical Physicists (MPs).
  • To define learning outcomes based on knowledge, skills, and competences (KSC approach) for AI in medical physics.
  • To provide a foundational and advanced curriculum applicable across various medical physics subspecialties.

Main Methods:

  • The curriculum is structured into two levels: Basic and Advanced, designed to be common across diagnostic and interventional radiology, nuclear medicine, and radiation oncology.
  • The Basic level introduces core AI concepts, development, and applications relevant to medical imaging and radiation therapy.
  • The Advanced level proposes a common block for further specialization within subspecialty curricula, with learning outcomes detailed in a syllabus format.

Main Results:

  • The Basic curriculum is stratified into four key areas: AI in medical imaging analysis, clinical implementation of AI, big data and enterprise imaging, and ethical/regulatory considerations.
  • The Advanced curriculum includes a common core module intended for elaboration by individual subspecialty curricula.
  • Learning outcomes are defined using a Knowledge, Skills, and Competences (KSC) approach and translated into a practical syllabus.

Conclusions:

  • This AI curriculum represents a pioneering effort to expand the educational framework for Medical Physicists in Europe.
  • It is intended as a supplementary document for subspecialty curricula and should be adopted by national training and regulatory bodies.
  • The proposed program can be implemented through the European School of Medical Physics Expert (ESMPE) and national EFOMP organizations to reach a wide audience.