ESTRO 2023 - Abstract Book


Saturday 13 May

ESTRO 2023

There are potential concerns related to the clinical use of AP. Automation of a task may result in an overrated and erroneous expectancy about easily redirecting resources/personnel towards other tasks. Safe and effective application of AP also requires continuous efforts to avoid generation of (slightly) suboptimal plans, that can remain unnoted for a long time. Another challenge is the loss of expertise in manual planning (which is also needed for AP configuration, especially for KBP). Apart from the need to maintain manual planning expertise, there is also the necessity for extensive training for optimal use of AP systems. This presentation aims at discussing challenges of AP, and possibilities to cope with them to ensure that AP can indeed solve problems rather than increase them. SP-0202 Will automation work in low- and middle-income countries? E. Titovich 1 1 International Atomic Energy Agency, Dosimetry and Medical Radiation Physics Section | Division of Human Health | Department of Nuclear Sciences and Applications, Vienna, Austria Abstract Text The application of artificial intelligence (AI) in healthcare has attracted extraordinary interest in recent years, and it is commonly acknowledged that AI is going to transform healthcare processes. A number of AI-based applications are currently being researched in several health service fields, and this interest is expected to increase and proliferate in the near future. The "health data ecosystem," which refers to the variety of data that might be used to create AI-based models, is also rapidly growing. Nevertheless, there is widespread concern that digital disparities continue to exist between developed and developing countries and that many low- and middle-income countries (LMICs) lack affordable access to information and communication technologies, particularly in healthcare. Considering the large inequalities between developed and developing countries in terms of access to health care as well as (digital) equipment and proper infrastructure availability, this digital gap might even increase with more AI-based products being introduced into clinical practice. Even though a wide number of processes involving radiation therapy and medical physics have been studied, there are still few AI-based clinical solutions commercially available. The deployment of AI-based tools in healthcare is facing numerous challenges in the technological, safety, ethical, and regulatory domains. Before considering the introduction of any advanced technological tool into clinical practice, its safety and efficacy should be validated, and a proper risk analysis should be performed. A core team of radiation medicine professionals is required to lead the selection and safe implementation of AI-based tools in the clinic. As a quantitative physical scientist in a clinical setting, the Clinically Qualified Medical Physicist (CQMP) should be considered a key contributor to this process. The Dosimetry and Medical Radiation Physics (DMRP) Section at the IAEA is working at different levels to provide instruments to CQMPs to be able to safely and effectively deal with AI-based tools in the near future. New guidelines for CQMPs as well as educational and training materials are currently under development with the support of experts in the field. Regardless of the income level of a country, to ensure a safe application of AI-based technologies in performing a specific medical physics task in a given clinical setting, it is currently recommended that a sufficient number of CQMPs with prior experience with "standard applications" of the task are locally available. Apart from the standard core education and training that is needed to become a CQMP, these physicists should also have adequate theoretical knowledge about the principles of AI as well as the practical skills for selecting, accepting, commissioning, and quality-assuring the AI-based tools. SP-0204 QuADRANT – a Study on Clinical Audit Uptake and Implementation Across Europe – Current Status and Recommendations on how to move forward. N. Jornet 1 , M. Coffey 2 , A. Bradly 3 , J. Clark 4 , F. Giammarile 5 , W. Wadsak 6 , M. Hierath 7 , P. Strojan 8 , D. Howlett 9 1 Hospital de la Santa Creu i Sant Pau, Radiofisica i Radioprotecció, Barcelona, Spain; 2 Trinity University, Trinity center for health sciences, Dublin, Ireland; 3 Mercy University Hospital, Radiology, Cork, Ireland; 4 European Society of Radiology (ESR), International affairs, Vienna, Austria; 5 IAEA, Human Health, Vienna, Austria; 6 Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria; 7 European Society of Radiology, International Affairs, Vienna, Austria; 8 Institute of Oncology Ljubljana, Radiation Oncology, Ljubljana, Slovenia; 9 East Sussex Healthacara NHC Trust, Radiology, Sussex, United Kingdom Abstract Text QuADRANT was a study funded by the European Commission to evaluate clinical audit uptake and implementation across Europe, with an emphasis on clinical audit as mandated within the BSSD (Basic Safety Standards Directive 2013/59/Euratom). The aim of this multiprofesional project was to obtain an overview of European clinical audit activity; identify good practices and resources, barriers and challenges; provide guidance and recommendations going forwards; Symposium: Clinical audits in radiation oncology: Current status and guidance for its implementation

Made with FlippingBook flipbook maker