Abstract gepubliceerd in Elsevier: Introduction: Recent research has identified the issue of ‘dose creep’ in diagnostic radiography and claims it is due to the introduction of CR and DR technology. More recently radiographers have reported that they do not regularly manipulate exposure factors for different sized patients and rely on pre-set exposures. The aim of the study was to identify any variation in knowledge and radiographic practice across Europe when imaging the chest, abdomen and pelvis using digital imaging. Methods: A random selection of 50% of educational institutes (n ¼ 17) which were affiliated members of the European Federation of Radiographer Societies (EFRS) were contacted via their contact details supplied on the EFRS website. Each of these institutes identified appropriate radiographic staff in their clinical network to complete an online survey via SurveyMonkey. Data was collected on exposures used for 3 common x-ray examinations using CR/DR, range of equipment in use, staff educational training and awareness of DRL. Descriptive statistics were performed with the aid of Excel and SPSS version 21. Results: A response rate of 70% was achieved from the affiliated educational members of EFRS and a rate of 55% from the individual hospitals in 12 countries across Europe. Variation was identified in practice when imaging the chest, abdomen and pelvis using both CR and DR digital systems. There is wide variation in radiographer training/education across countries.
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Poster KIM voor de ECR is nu online te zien via EPOS: https://epos.myesr.org/poster/esr/ecr2022/C-16092 posternummer: C-16092, ECR 2022 Purpose Artificial Intelligence (AI) has developed at high speed the last few years and will substantially change various disciplines (1,2). These changes are also noticeable in the field of radiology, nuclear medicine and radiotherapy. However, the focus of attention has mainly been on the radiologist profession, whereas the role of the radiographer has been largely ignored (3). As long as AI for radiology was focused on image recognition and diagnosis, the little attention for the radiographer might be justifiable. But with AI becoming more and more a part of the workflow management, treatment planning and image reconstruction for example, the work of the radiographer will change. However, their training (courses Medical Imaging and Radiotherapeutic Techniques) hardly contain any AI education. Radiographers in the Netherlands are therefore not prepared for changes that will come with the introduction of AI into everyday work.
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Abstract: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. Clinical relevance statement: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. Key Points: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.
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