Expanding the medical physicist curricular and professional programme to include Artificial Intelligence

Abstract

To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs). Materials and methods: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach). Results: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics;(2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications. Conclusions: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules andto some extentalso by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

“…The knowledge of regulatory requirements beyond radiation safety should be part of the (future) core competences of the MPE. A whole new curriculum is being established [52]. The interested reader can refer to a recently published article on the subject [53].…”

“…This task is however not easy and not yet well developed. MPEs must be trained with regard to basic and advanced concepts of AI algorithms and applications [52,55] and have to team up with the medical teams for performing these tasks. It is also extremely important that regulatory agencies and industry jointly develop robust AI standards of practice and transparent insertion rules [56].…”

The perceptions of medical physicists towards relevance and impact of artificial intelligence

“…European Federation of Organisations For Medical Physics (EFOMP) white paper proposed that training and education programs are needed to prepare the medical physicist for this revolutionary transformation in healthcare and medicine [11]. Based on this white paper, the curriculum has been discussed, aiming its expansion to include AI for the education and training of European medical physicists, based on their needs [12,13] The practice and daily routine of medical physicists were also affected by AI [2]. Big data and deep learning will profoundly change the profession in general and future research [11].…”

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

“…Students should be provided with courses focusing more on quantitative skills, incorporating machine learning and artificial intelligence, mathematical modeling, computation and simulation, and big data [25]. This is true both at the undergraduate and postgraduate level, with several medical specializations (like radiology, oncology, physical medicine, rehabilitation, rheumatology, cardiology, or urology, among others) expanding current knowledge levels, skills, and competencies [26,27]. Attempts to redesign specialist syllabus are very recent, but it can be anticipated that they will have a great impact on future medical practice.…”