Journal of Medical Radiation Sciences最新文献

{"title":"Improving Equity of Care for Culturally and Linguistically Diverse Patients: Utilising Multilingual Software to Support Breath-Hold Breast Radiation Therapy","authors":"Toby Lowe,&nbsp;Rory Hartley,&nbsp;John Atyeo,&nbsp;Brooke Griffiths,&nbsp;Brian Porter,&nbsp;Gillian Lamoury,&nbsp;Marita Morgia,&nbsp;Gabrielle Metz,&nbsp;Susan Carroll","doi":"10.1002/jmrs.70095","DOIUrl":"10.1002/jmrs.70095","url":null,"abstract":"<p>Breath-hold techniques, including Deep Inspiration Breath Hold (DIBH) and Exhalation Breath Hold (EBH), are commonly used in radiation therapy to reduce radiation exposure to organs at risk while maintaining accurate tumour targeting. However, high failure rates during breath-hold simulations have been associated with patient anxiety and insufficient preparation—particularly when instruction is given only 20 min prior to the procedure. These challenges are amplified for patients from culturally and linguistically diverse (CALD) backgrounds. To address this, a multilingual mobile application (app) has been developed to improve patient education, communication, and compliance, with a particular focus on supporting CALD patients. Replacing traditional pamphlets with interactive instructional videos, visual aids, and real-time translation in seven languages, the app allows patients to familiarise themselves with breath-hold techniques before their Computed Tomography (CT) simulation. A General Radiation Therapy (RT) section in the app further supports communication between radiation therapists (RTs) and their patients by providing translated commands for standard patient positioning and movement instructions, improving accuracy and workflow efficiency. The app integrates with in-room sound systems, allowing RTs to provide remote coaching while maintaining clear and consistent communication. Additionally, the Frequently Asked Questions (FAQ) section of the app addresses common concerns that patients have about radiation therapy, helping them gain a better understanding with the possibility of reducing anxiety. The app has been implemented at the Northern Sydney Cancer Centre (NSCC) and is expected to improve patient compliance in breath-hold techniques and streamline clinical workflows.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"352-358"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148218128","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":"Australian Society of Medical Imaging and Radiation Therapy Image Registration in Radiation Therapy Position Paper","authors":"Tamara Barry,&nbsp;Huong Nguyen,&nbsp;Laurel Schmidt,&nbsp;Kate Stewart,&nbsp;Nigel Anderson","doi":"10.1002/jmrs.70071","DOIUrl":"10.1002/jmrs.70071","url":null,"abstract":"<p>The report from the American Association of Physicists in Medicine (AAPM) Task Group 132, published in 2017, established a framework and recommendations for the safe implementation of rigid image registration (RIR) and deformable image registration (DIR) into radiation oncology clinical practice. The Medical Image Registration Special Interest Group (MRSIG) of the Australasian College of Physical Scientists and Engineers in Medicine further built on these recommendations through the publishing of best practice guidelines for DIR warping based on increased accessibility to such tools in the clinical environment. There remains an increasing responsibility on radiation therapists, a critical member of the radiation oncology multidisciplinary team, to safely embed RIR and DIR process into their routine clinical practice, along all steps of the patient's radiation therapy treatment journey. This position paper, authored by the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) Image Registration Working Party and endorsed by the ASMIRT Executive, (i) details the role of radiation therapists in the application of RIR and DIR in day-to-day clinical practice and (ii) delivers a series of recommendations to support the safe implementation of RIR and DIR into radiation therapist workflows.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"345-351"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146220180","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":"Continuing Professional Development – Medical Imaging","authors":"","doi":"10.1002/jmrs.70112","DOIUrl":"10.1002/jmrs.70112","url":null,"abstract":"<p>Maximise your continuing professional development (CPD) by reading the selected article and answering the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 73, Issue 4, December 2026.</p><p>Scan this QR code or https://www.surveymonkey.com/r/JMRS_Sept2026_MI to find the answers</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"375-376"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148447528","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":"Shaping the Future of Radiography Education: Lessons From ChatGPT and Generative AI","authors":"Minh T. Chau,&nbsp;Haydn Kerr,&nbsp;Clare L. Singh,&nbsp;Bismark Ofori-Manteaw,&nbsp;Elio Arruzza,&nbsp;Kelly Bentley-Spuur","doi":"10.1002/jmrs.70075","DOIUrl":"10.1002/jmrs.70075","url":null,"abstract":"<p>Generative artificial intelligence (AI), particularly large language models such as ChatGPT, is increasingly influencing learning and continuing professional development (CPD) across health professions. The radiography discipline is well positioned to explore the benefits and limitations of these technologies. This article presents a narrative review and conceptual synthesis of emerging evidence on the use of generative artificial intelligence in radiography education. Drawing on emerging radiography-specific studies, the paper synthesises evidence relating to image critique, professional communication training, simulation-based learning, CPD planning and reflective practice. Evidence suggests that ChatGPT can provide structured guidance, support self-assessment and scaffold learning processes that bridge classroom knowledge and clinical expectations. In image evaluation tasks, generative AI may identify broad positioning and exposure issues and prompt metacognitive reasoning when learners are encouraged to critically appraise its outputs. In communication tasks, AI systems can offer structural support but consistently lack the relational nuance essential for safe clinical interactions. In reflective tasks, AI-generated reflections typically lack emotional depth, professional and ethical insight and accountability. Furthermore, custom AI-driven simulations show promise as tools for safe experimentation; however, their outputs commonly default to technically correct responses that provide limited contextual or professional insight. These findings suggest that the value of generative AI lies in how it is pedagogically framed. Rather than advancing algorithmic or performance-based claims, this paper interprets generative AI as a pedagogical artefact whose educational value is contingent on framing, educator oversight and learner critical engagement. The review integrates fragmented evidence with educational theory and professional regulation and proposes a conceptual framework for the responsible integration of generative AI in radiography education. By foregrounding AI literacy, ethical governance and professional accountability, this paper offers an integrative, design-level contribution to support educators and professional bodies navigating the adoption of generative AI across the radiography education continuum.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"327-334"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147317352","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":"Radiation Dose Optimisation Through Artificial Intelligence (AI)-based Auto-Thorax Collimation","authors":"Derek (Yoon-Sang) Lee,&nbsp;Nicole Chan,&nbsp;Richard Knight,&nbsp;Christopher Hayre","doi":"10.1002/jmrs.70088","DOIUrl":"10.1002/jmrs.70088","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Artificial intelligence (AI) is increasingly adopted in digital radiography to enhance workflow efficiency, standardisation and support for radiation dose optimisation. Auto-thorax collimation (ATC) has been introduced to automate field size selection in chest X-ray (CXR) imaging; however, its clinical performance and impact on collimation consistency in routine chest X-ray practice require further evaluation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A single-centre retrospective study analysed 400 posteroanterior (PA) erect CXRs sourced from the Picture Archiving and Communication System (PACS) and local image archives. Collimation size was measured in both the superior–inferior and lateral dimensions. An additional, 200 erect CXRs were collected to assess the repeat rate due to collimation errors. Statistical analysis was performed using two-sample <i>t</i>-tests for collimation measurements and a two-proportion <i>Z</i>-test for repeat rates. The coefficient of variation (CV) was calculated to quantify the variability in collimation field size for each operator across both ATC and manual collimation methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ATC demonstrated tighter inferior and left lateral collimation than manual collimation (<i>p</i> &lt; 0.05), while manual collimation showed tighter superior collimation. Collimation consistency was greater with ATC, as evidenced by lower CV values across operators. Repeat rates were comparable between ATC (7%) and manual collimation (8%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>AI-based collimation provides collimation results similar to manual performance, with improved standardisation and reproducibility. Its consistent output suggests potential benefits in high-turnover environments, enhancing workflow efficiency whilst optimising radiation safety.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"300-308"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147529982","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":"Impact of 1024-Matrix Size on Perforating Artery Visualisation in Cerebral Computed Tomography Angiography Using a 64-Slice CT Scanner","authors":"Hokuto Nagumo,&nbsp;Yuki Nagata,&nbsp;Mayumi Maruko,&nbsp;Jun Sakai,&nbsp;Yusuke Fujiwara,&nbsp;Joma Oikawa","doi":"10.1002/jmrs.70055","DOIUrl":"10.1002/jmrs.70055","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Computed tomography angiography (CTA) is essential for preoperative evaluation of intracranial vessels; however, the visualisation of small perforating arteries remains challenging with conventional systems. This study investigated whether increasing the reconstruction matrix size from 512 to 1024 pixels improves the visualisation of perforating arteries in cerebral CTA using a standard 64-slice computed tomography (CT) scanner.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study comprised phantom and clinical components. Physical image properties were assessed through Task Transfer Function (TTF) and Noise Power Spectrum (NPS) analyses. In the clinical phase, 41 patients underwent cerebral CTA with both 512- and 1024-matrix reconstructions. Quantitative analysis was performed to determine the peak CT values of the anterior choroidal artery and standard deviation in the basal cistern. Two independent observers (CT radiographers) evaluated the visualisation of anterior choroidal and posterior thalamoperforating arteries using a 5-point scale.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Physical metrics showed minimal differences between matrix sizes, with 5% TTF values of 0.862 and 0.867 cycles/mm for 512 and 1024 pixels, respectively. The anterior choroidal artery exhibited higher peak CT values with 1024 pixels (139.31 ± 27.02 HU vs. 136.85 ± 26.69 HU, <i>p</i> &lt; 0.001). The qualitative assessment revealed significant improvements in vessel visualisation with 1024 pixels, particularly for the posterior thalamoperforating artery, where high-quality visualisation increased from 17.07% to 39.03%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Although increasing the matrix size from 512 to 1024 pixels did not significantly affect the physical image quality metrics, it enhanced the visual detection of small perforating arteries through enhanced sampling density. This optimization technique offers a practical approach for enhancing preoperative vessel evaluation using existing CT technology without requiring specialised equipment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"263-269"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473898","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":"Chest X-Ray Imaging Algorithms for Optimising the Visualisation of Medical Lines and Tubes: A Study With Student Radiographers","authors":"Vanessa Pui,&nbsp;John Robinson,&nbsp;Amanda Punch,&nbsp;Don Nocum,&nbsp;Dania Abu Awwad","doi":"10.1002/jmrs.70086","DOIUrl":"10.1002/jmrs.70086","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Chest X-rays (CXRs) play a crucial role in determining the placement of medical lines and tubes. However, visualisation of these devices can be limited due to imaging and patient factors, increasing the risk of undetected misplacements, repeated radiation exposure and delayed patient management. These challenges may be more pronounced for student radiographers who are still developing their radiography skills. Although X-ray post-processing algorithms, such as grey-scale inversion (GSI) and Advanced Edge Enhancement (AEE), have the potential to optimise line and tube conspicuity, their effectiveness is not well established. Hence, this study aimed to evaluate the usefulness of GSI and AEE algorithms in enhancing visual image quality and reader confidence among student radiographers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Twenty standard CXRs, demonstrating lines or tubes, were copied and processed using GSI and AEE algorithms. Images were paired as follows: Setting 1 (Standard—GSI), Setting 2 (Standard—AEE), Setting 3 (GSI—AEE). In each setting, participants used a relative visual grading characteristics (VGC) assessment to compare algorithms and indicated reader confidence using a 5-point Likert scale.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Twenty-five students completed the VGC assessment. The Friedman test revealed a significant increase in reader confidence (<i>χ</i>² (3) = 48.02, <i>p</i> &lt; 0.001) in Settings 2 and 3. VGC analysis showed significantly higher visual grading scores with both GSI (AUC_VCG = 0.72, <i>p</i> &lt; 0.001) and AEE (AUC_VCG = 0.91, <i>p</i> &lt; 0.001) compared with the standard CXRs, with AEE outperforming GSI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>GSI and AEE appear to be valuable tools that merit integration into imaging protocols to promote best practices by optimising the use of resources, supporting diagnostic accuracy and potentially helping to minimise radiation exposure.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"291-299"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574365","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":"Comparative Evaluation of Four Stereotactic Radiosurgery Planning Approaches for Treatment of Posterior Choroidal Melanoma","authors":"Robert Nigro,&nbsp;Kenton Thompson,&nbsp;Cathy Markham,&nbsp;Adam U. Yeo,&nbsp;Daniel Sapkaroski,&nbsp;Claire Phillips","doi":"10.1002/jmrs.70076","DOIUrl":"10.1002/jmrs.70076","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This was a comparative treatment planning study for posterior choroidal melanoma (CM) using four stereotactic approaches: Brainlab iPlan dynamic conformal arc therapy (DCAT), Brainlab Elements modulated arcs (BE), Varian Eclipse HyperArc modulated arcs (HA) and Gamma Knife ICON Leksell GammaPlan (GK). The clinical goal was to reduce dose to normal brain whilst maintaining other treatment planning goals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Thirteen CM cases over 3 years (2015–2018) treated with DCAT prescribed 50 Gy in 5 fractions were selected. The DCAT technique involved a forward planning method whereas semi-automated methods were used in BE, HA and GK. Target coverage and dose to critical organs were evaluated and compared across all approaches with the same set of structures used for each.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Volume of brain receiving 10 Gy was DCAT 0.93 cc and 0.00 cc for GK, BE, HA (<i>p</i> &lt; 0.001). Brain D_near−max GK 5.52 Gy, HA 6.41 Gy, BE 7.28 Gy, DCAT 13.73 Gy (<i>p</i> = 0.001). The median Gradient Index (GI) was GK 2.79, DCAT 6.39, BE 5.07, HA 6.00 (<i>p</i> &lt; 0.001) with GK significantly different from each of the other approaches for PTV parameters. Further statistical significance was recorded for the lacrimal gland, contralateral lens and ipsilateral optic nerves.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Modulation tools in HA and BE enabled clinically meaningful reduction in dose to brain compared with DCAT whilst maintaining dose to PTV. GK produced highest PTV coverage with superior gradient index, however current methods for eye immobilisation and treatment delivery only allow for single fraction treatment with GK.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"250-258"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467727","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":"Facilitating Computed Tomography Education in Clinical Practice","authors":"Matthew Russell,&nbsp;Dania Abu Awwad,&nbsp;Minh Chau,&nbsp;Frances Gray,&nbsp;Adrian Repole,&nbsp;Yobelli Alexandra Jimenez","doi":"10.1002/jmrs.70094","DOIUrl":"10.1002/jmrs.70094","url":null,"abstract":"<p>Clinical placements are vital for radiography students to consolidate knowledge and develop computed tomography (CT) skills. Whilst CT principles are commonly introduced in university curricula, teaching approaches during clinical placements remain variable and under-described. Clinical radiographers supervising students often balance service delivery with education, frequently without structured guidance. This educational article presents a practical framework to support CT education in clinical settings, organised into five interrelated domains: psychological safety, logistics, key CT teaching points, approaches to clinical instruction and competence assessment. Drawing on existing literature and authors' collective clinical and academic experience, the paper outlines strategies embedded within routine CT workflow to support intentional and staged student learning. Practical examples include orientating students to the CT environment, establishing baseline knowledge, aligning teaching with clinical workflow, scaffolding learning according to student readiness and using performance-based assessment methods appropriate to workplace learning. Challenges, including limited scanner access, variable student preparedness and time constraints, are addressed with pragmatic recommendations to support learning. The framework aims to support consistent, learner-centred CT education and strengthen collaboration between clinical supervisors and academic educators. Our team's future work will focus on developing co-designed resources for clinical CT education, designed to be cross-institutional and adaptable to local needs.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"359-372"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147874371","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":"The Role of Language in Shaping Cultural Perceptions Within Healthcare and Supporting Neurodivergent People's Well-being and Access to Care: Focus on Autistic Experiences","authors":"Ben Potts,&nbsp;C. J. Parish,&nbsp;Nmesoma Francisca Ukaji,&nbsp;Georgia Pavlopoulou,&nbsp;Themis Karaminis,&nbsp;Matthew Lane,&nbsp;James McStravick,&nbsp;Madeline O'Brien,&nbsp;Jane Harvey-Lloyd,&nbsp;Sophie Shephard,&nbsp;Jane Green,&nbsp;Anne Collis,&nbsp;Nikolaos Stogiannos,&nbsp;Emily Skelton,&nbsp;Christina Malamateniou","doi":"10.1002/jmrs.70096","DOIUrl":"10.1002/jmrs.70096","url":null,"abstract":"<p>Chronic stigmatisation and social exclusion of neurodivergent people have resulted in poorer quality of life and adverse health outcomes. Language has been weaponised and has furthered their suffering and isolation. With this paper, we propose some considerations of optimal communication interactions in healthcare with neurodivergent people, in general, and autistic people, in particular, to ensure respectful and human-centred patient and practitioner interactions across both clinical and academic practice.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"73 3","pages":"243-249"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147973073","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}