Radiography最新文献

{"title":"CT metal artefact reduction for hip and shoulder implants using novel algorithms and machine learning: A systematic review with pairwise and network meta-analyses","authors":"","doi":"10.1016/j.radi.2024.10.009","DOIUrl":"10.1016/j.radi.2024.10.009","url":null,"abstract":"<div><h3>Introduction</h3><div>Many tools have been developed to reduce metal artefacts in computed tomography (CT) images resulting from metallic prosthesis; however, their relative effectiveness in preserving image quality is poorly understood. This paper reviews the literature on novel metal artefact reduction (MAR) methods targeting large metal artefacts in fan-beam CT to examine their effectiveness in reducing metal artefacts and effect on image quality.</div></div><div><h3>Methods</h3><div>The PRISMA checklist was used to search for articles in five electronic databases (MEDLINE, Scopus, Web of Science, IEEE, EMBASE). Studies that assessed the effectiveness of recently developed MAR method on fan-beam CT images of hip and shoulder implants were reviewed. Study quality was assessed using the National Institute of Health (NIH) tool. Meta-analyses were conducted in R, and results that could not be meta-analysed were synthesised narratively.</div></div><div><h3>Results</h3><div>Thirty-six studies were reviewed. Of these, 20 studies proposed statistical algorithms and 16 used machine learning (ML), and there were 19 novel comparators. Network meta-analysis of 19 studies showed that Recurrent Neural Network MAR (RNN-MAR) is more effective in reducing noise (LogOR 20.7; 95 % CI 12.6–28.9) without compromising image quality (LogOR 4.4; 95 % CI -13.8-22.5). The network meta-analysis and narrative synthesis showed novel MAR methods reduce noise more effectively than baseline algorithms, with five out of 23 ML methods significantly more effective than Filtered Back Projection (FBP) (<em>p</em> &lt; 0.05). Computation time varied, but ML methods were faster than statistical algorithms.</div></div><div><h3>Conclusion</h3><div>ML tools are more effective in reducing metal artefacts without compromising image quality and are computationally faster than statistical algorithms. Overall, novel MAR methods were also more effective in reducing noise than the baseline reconstructions.</div></div><div><h3>Implications for practice</h3><div>Implementation research is needed to establish the clinical suitability of ML MAR in practice.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical insights into cross-sectional imaging integration in radiography education","authors":"","doi":"10.1016/j.radi.2024.10.019","DOIUrl":"10.1016/j.radi.2024.10.019","url":null,"abstract":"<div><h3>Introduction</h3><div>Clinical practice is a critical component of radiography curricula, offering students with essential skills and training for proficient practice. Educational institutions are challenged to review and develop strategies to meet evolving service demands regularly. This study aims to gain insight into the inclusion of cross-sectional imaging within pre-registration radiography training.</div></div><div><h3>Methods</h3><div>An online questionnaire, based on previous European Federation of Radiographer Societies (EFRS) surveys, included closed-ended questions and ascertained the level of qualification, cross-sectional imaging incorporation, and tasks and assessments within programmes. The questionnaire was distributed through the EFRS Research Hub at the European Congress of Radiology (ECR) 2023 and online via social media. Closed-ended questions were summarised using descriptive statistics.</div></div><div><h3>Results</h3><div>Responses were received from 64 individual radiography educators across 29 different countries. Fifty-seven respondents (91.9 %) reported including cross-sectional imaging training in their institution's pre-registration radiography programme. An increase in the amount of time dedicated to clinical training in cross-sectional imaging was reported by 24 respondents (42.1 %). Overall, 32 individuals (53.3 %) stated that CT is a specialised modality, and dedicated training should be for radiographers once they obtain their basic qualifications. In contrast, 36 respondents (61 %) agreed that MRI should also be reserved as a specialised modality.</div></div><div><h3>Conclusion</h3><div>Study findings indicate a lack of consistency among pre-registration radiography programmes in terms of how they include cross-sectional imaging in their curricula. Differing opinions on this issue are likely to be guided by national standards and workforce requirements upon qualification.</div></div><div><h3>Implications for practice</h3><div>Variations in training curricula can present significant challenges for graduates. To align with the most recent Standards of Proficiency, curricula must be regularly reviewed and updated. Such Standards now typically require radiographers to perform a range of CT scans, including those of the head, C-spine, chest, and abdomen. Therefore, integrating comprehensive training in cross-sectional imaging into pre-registration education is crucial to ensure that future professionals meet these essential competencies and are fully prepared for their roles.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paediatric diagnostic reference levels for common computed tomography procedures: A systematic review","authors":"","doi":"10.1016/j.radi.2024.09.058","DOIUrl":"10.1016/j.radi.2024.09.058","url":null,"abstract":"<div><h3>Background</h3><div>Previous paediatric diagnostic reference levels (PDRL) literature reviews for commonly performed procedures of the brain, chest and abdomen revealed wide DRL variation and deviation of scanning protocols across CT centres. The current review went further to determine the extent and possible factors of DRL variation in the same procedure, age or weight group, between scanners and CT centres for the standardisation of CT practice globally.</div></div><div><h3>Methods</h3><div>The preferred reporting items for systematic reviews and meta-analysis (PRISMA) flow chart was used to screen articles in Science Direct, Medline, Academic Search Complete via EBSCOhost, PubMed, and CINAHL via EBSCOhost including the Google search engine.</div></div><div><h3>Results</h3><div>A total of 6573 articles were retrieved and screened against the established criteria and finally, 52 articles were selected and synthesised narratively.</div></div><div><h3>Conclusion</h3><div>The findings of this review show variation of brain PDRLs up to a factor of 2 fold for the same examination and age group. Factors attributable to dose variations noted in this review were largely related to the setting of the scan protocols such as the use of different phantom sizes, dose parameters, and age groups. This indicates the need to standardise methods of establishing PDRLs and alignment with the European Commission and ICRP recommended guidelines are proposed.</div></div><div><h3>Implication for practice</h3><div>The review highlights different methods for establishing PDRLs and their implication which could guide radiographers and medical physicists in future PDRLs establishment for dose optimization.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Making it work in the face of extreme adversity” - Exploring perceptions for the future of the imaging and oncology workforce using ‘soundbite’ interviews","authors":"","doi":"10.1016/j.radi.2024.10.017","DOIUrl":"10.1016/j.radi.2024.10.017","url":null,"abstract":"<div><h3>Background</h3><div>Public demand and scrutiny, an aging population, inefficient funding and the legacy of Covid-19 are just some of the challenges the United Kingdom's health service faces. In imaging and oncology, there has been an exponential growth in service need against a workforce which is struggling to recruit and retain. This project aims to explore what the current, and future, workforce perceive the main opportunities and solutions, threats and risks are.</div></div><div><h3>Method</h3><div>Very short structured ‘soundbite’ interviews were employed to capture brief opinions or ‘snippets’ of dialogue. Participants recruited at a large imaging and oncology congress were asked what they considered the most significant opportunity/solution and threat/risk related to the future workforce. Descriptive and content analysis was undertaken to provide evaluation of role, regions, and frequency of themes.</div></div><div><h3>Results</h3><div>88 ‘soundbite’ interviews were undertaken lasting between 30 s and 4 min in length. The most common themes relating to opportunities/solutions considered education and students, workforce development and skill mix, and the use of technology. The most common threats/risks were identified as a lack of support for the workforce, recruitment and retention, national strategic issues, and barriers to workforce development.</div></div><div><h3>Conclusion</h3><div>The current workforce perceives a greater number of threats/risks for the future than potential opportunities/solutions. In particular, burnout and staff attrition were the most frequent perceptions of risk, though role development was often highlighted as the biggest opportunity. Interestingly AI and technology were frequently considered both opportunity and threat.</div></div><div><h3>Implications for practice</h3><div>This study highlights that a lot needs to be done to support our future workforce and make best use of the potential opportunities and solutions.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors of importance for the development of evidence-based practice amongst radiographers in public healthcare","authors":"","doi":"10.1016/j.radi.2024.10.011","DOIUrl":"10.1016/j.radi.2024.10.011","url":null,"abstract":"<div><h3>Introduction</h3><div>Research evidence suggests that radiographers often rely on previous training, traditional practices, work experience and protocols developed within the department rather than up-to-date research-based evidence in their daily practice. The aim of the study was to investigate factors that might impact the development of evidence-based practice amongst radiographers in clinical public settings in the Nordic countries.</div></div><div><h3>Methods</h3><div>An online survey was performed amongst 640 radiographers in four Nordic countries. Multivariate logistic regression was performed to investigate the odds ratio (OR) of facilitators for and barriers to radiographers' development of evidence-based practice.</div></div><div><h3>Results</h3><div>A reflective approach in everyday practice and being aware of the current research evidence were significant facilitators for radiographers' development of evidence-based practice (OR ≥ 3.10, p &lt; 0.001). Discussing research with colleagues and managers was associated with engagement in the utilisation of evidence (OR 7.21, p &lt; 0.001). Difficulties in evaluating research evidence represented the only significant barrier (OR 1.84, p 0.009).</div></div><div><h3>Conclusion</h3><div>A critical approach amongst radiographers in their performance of healthcare in diagnostic imaging, and the development of their academic skills to improve awareness of the available research evidence are important factors for developing evidence-based practice in radiography. Leadership is crucial for the engagement of radiographers in the development of evidence-based practice. Management should facilitate the development of a learning culture within diagnostic imaging.</div></div><div><h3>Implications for practice</h3><div>The results provide suggestions for the development of a learning culture, proactive and person-centred leadership, and strategic management for the provision of research infrastructure, all of which contribute to the further integration of evidence-based practice in radiography. Also, the study results suggest the importance of shared responsibility for creating a critical fellowship in diagnostic imaging.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI","authors":"","doi":"10.1016/j.radi.2024.10.010","DOIUrl":"10.1016/j.radi.2024.10.010","url":null,"abstract":"<div><h3>Introduction</h3><div>In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (HCPC 2023; standard 12.25). With the rapid deployment of AI in departments, staff must be prepared to implement and utilise AI. AI readiness is crucial for adoption, with education as a key factor in overcoming fear and resistance. This survey aimed to assess the current understanding of AI among students and qualified staff in clinical practice.</div></div><div><h3>Methods</h3><div>A survey targeting radiographers (diagnostic and therapeutic), radiologists and students was conducted to gather demographic data and assess awareness of AI in clinical practice. Hosted online via JISC, the survey included both closed and open-ended questions and was launched in March 2023 at the European Congress of Radiology (ECR).</div></div><div><h3>Results</h3><div>A total of 136 responses were collected from participants across 25 countries and 5 continents. The majority were diagnostic radiographers 56.6 %, followed by students 27.2 %, dual-qualified 3.7 % and radiologists 2.9 %. Of the respondents, 30.1 % of respondents indicated that their highest level of qualification was a Bachelor's degree, 29.4 % stated that they are currently using AI in their role, whilst 27 % were unsure. Only 10.3 % had received formal AI training.</div></div><div><h3>Conclusion</h3><div>This study reveals significant gaps in training and understanding of AI among medical imaging staff. These findings will guide further research into AI education for medical imaging professionals.</div></div><div><h3>Implications for practice</h3><div>This paper lays foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Children and adolescents’ experiences of active participation in radiological examinations -a qualitative study","authors":"","doi":"10.1016/j.radi.2024.10.016","DOIUrl":"10.1016/j.radi.2024.10.016","url":null,"abstract":"<div><h3>Introduction</h3><div>Children and adolescents have the right to participate in decisions about their health, including during radiological examinations. This study explores their participation experiences in this context.</div></div><div><h3>Methods</h3><div>This qualitative field study examines the importance of active participation from a Child-Centered Care perspective. Fostering active participation requires supportive structures that recognize each child as a unique social actor. Data was collected through observations and semi-structured interviews with 10 children and adolescents diagnosed with cystic fibrosis undergoing High Resolution Computed Tomography (CT) scans. Thematic analysis was performed on the transcribed data to identify central themes and patterns.</div></div><div><h3>Results</h3><div>Parental presence and humor during CT scans helped reduce anxiety among participants. Key factors influencing participation included examination duration and pain, with many expressing a desire for greater involvement, especially during longer, more painful procedures. Few children reported experiencing active participation in hospital settings, particularly during CT scans. Younger and more expressive participants tended to have more opportunities for involvement. While most desired active participation during hospital visits and CT scans, they showed less interest in making treatment decisions.</div></div><div><h3>Conclusion</h3><div>The radiographer's affirming and humorous approach is essential, as are considerations of children and adolescents' preferences regarding parental presence, examination duration, and pain management. Participation levels vary, and limited opportunities can undermine their rights. Children and adolescents express a strong desire for active participation in hospital and radiological settings but often feel insecure about making treatment decisions.</div></div><div><h3>Implications for practice</h3><div>This study highlights critical issues related to children and adolescents’ participation in radiological examinations, offering valuable insights for healthcare professionals to enhance participation, which is a fundamental right and crucial aspect of their care.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entrepreneurial thinking in radiography: Developing an imaging facility to support the future workforce","authors":"","doi":"10.1016/j.radi.2024.10.012","DOIUrl":"10.1016/j.radi.2024.10.012","url":null,"abstract":"<div><h3>Objectives</h3><div>This article explores the significance of recognising and utilising entrepreneurial attributes—such as knowledge, skills, talent, and experience—to develop radiography education guided by a LUCID framework. It aims to demonstrate how enterprising behaviours and competencies can enhance human actions and address healthcare challenges, thereby improving employability in line with the College of Radiographers Education and Career Framework and industry demands.</div></div><div><h3>Key findings</h3><div>The article defines the concepts of Enterprise and Entrepreneurship and discusses the importance of understanding one's accumulated skills and experiences, known as Human Capital, for personal and professional growth. It illustrates how entrepreneurial thinking and utilisation of the LUCID framework facilitated the development of an imaging facility, which reflects a commitment to innovation and excellence in radiography education.</div></div><div><h3>Conclusion</h3><div>The article concludes that adopting entrepreneurial practices and reflecting on one's human capital can significantly benefit radiographers and educators. This approach not only enhances personal and professional development but also adds value to the profession, employers, and patients.</div></div><div><h3>Implications for practice</h3><div>Radiographers and educators are encouraged to adopt entrepreneurial practices and reflect on their human capital to identify areas for improvement. This can lead to better healthcare outcomes, improved employability, and alignment with industry demands and the College of Radiographers Education and Career Framework.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development","authors":"","doi":"10.1016/j.radi.2024.10.008","DOIUrl":"10.1016/j.radi.2024.10.008","url":null,"abstract":"<div><h3>Objectives</h3><div>Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry perspectives, this paper provides recommendations for medical imaging undergraduate education, including curriculum revision and re-alignment.</div></div><div><h3>Key findings</h3><div>A proposed modular framework is outlined to assist course providers in integrating AI into university programs. An example course design includes modules on data science fundamentals, machine learning, AI ethics and patient safety, governance and regulation, AI tool evaluation, and clinical applications. A proposal to embed these longitudinally in the curriculum combined with hands-on experience and work-integrated learning will help develop the necessary knowledge of AI and its real-world impacts. Authentic assessment examples reinforce learning, such as critically appraising published research and reflecting on current technologies. Maintenance of an up-to-date curriculum will require a collaborative, multidisciplinary approach involving educators, clinicians, and industry professionals.</div></div><div><h3>Conclusion</h3><div>Integrating AI education into undergraduate medical imaging programs equips future radiographers in an evolving technological landscape. A strategic approach to embedding AI modules throughout degree programs assures students a comprehensive understanding of AI principles, skills in utilising AI tools effectively, and the ability to critically evaluate their implications.</div></div><div><h3>Implications for practice</h3><div>The practical implementation of undergraduate AI education will prepare radiographers to incorporate these technologies while assuring patient care.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical imaging and radiation science students' use of artificial intelligence for learning and assessment","authors":"","doi":"10.1016/j.radi.2024.10.006","DOIUrl":"10.1016/j.radi.2024.10.006","url":null,"abstract":"<div><h3>Introduction</h3><div>Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography students' use of artificial intelligence for learning and assessment. Therefore, this study aimed to gain an understanding of this phenomenon.</div></div><div><h3>Methods</h3><div>The study used a qualitative explorative and descriptive research design. Data was obtained through five focus group interviews with purposively sampled undergraduate medical imaging and radiation science students at a single higher education institution in South Africa. Verbatim transcripts of the audio-recorded interviews were analysed thematically.</div></div><div><h3>Results</h3><div>Three themes and related subthemes were developed: 1) understanding artificial intelligence, 2) experiences with the use of artificial intelligence with the subthemes of the use of artificial intelligence in theoretical and clinical learning and challenges of using artificial intelligence, and 3) incorporation of artificial intelligence in undergraduate medical imaging and radiation sciences education with the subthemes of student education, ethical considerations and responsible use and curriculum integration of artificial intelligence in relation to learning and assessment.</div></div><div><h3>Conclusion</h3><div>Participants used artificial intelligence for learning and assessment by generating ideas to enhance academic writing, as a learning tool, finding literature, language translation and for enhanced efficiency. Simulation-based artificial intelligence supports students' clinical learning, and artificial intelligence within the clinical departments assists with improved patient outcomes. However, participants expressed concerns about the reliability and ethical implications of artificial intelligence-generated information. To address these concerns, participants suggested integrating artificial intelligence into medical imaging and radiation sciences education, where educators need to educate students on the responsible use of artificial intelligence in learning and consider artificial intelligence in assessments.</div></div><div><h3>Implications for practice</h3><div>The study findings contribute to understanding medical imaging and radiation sciences students’ use of artificial intelligence and may be used to develop evidence-based strategies for integrating artificial intelligence into the curriculum to enhance medical imaging and radiation sciences education and support students.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}