Unlocking Digital Future of Education: A Global Review of AI and VR Education in Diagnostic and Interventional Radiology Postgraduate Training

Unlocking Digital Future of Education: A Global Review of AI and VR Education in Diagnostic and Interventional Radiology Postgraduate Training | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unlocking Digital Future of Education: A Global Review of AI and VR Education in Diagnostic and Interventional Radiology Postgraduate Training Mohammed Bilal, Rayhan Yousef Gasiea, Abdual Rahman Alvi, Professor Mohamad Hamady, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8185340/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose: As artificial intelligence (AI) and virtual reality (VR) continue to expand in radiology, structured education is essential for preparing radiologists (Tejani et al., 2022). Despite calls for formal training, the extent to which these technologies are integrated into postgraduate curricula remains unclear. This study assessed the depth and scope of AI and VR education within official postgraduate radiology curricula. Methods: We analysed 11 official postgraduate radiology curricula from 16 countries across diverse economic and geographic contexts, including the UK, Malaysia, Nigeria, and shared frameworks such as the ESR curriculum (Austria, Switzerland, and others) and the RANZCR curriculum for Australia/New Zealand. Publicly available national training documents were reviewed. Data were extracted using a structured proforma that assessed AI and VR content by depth (0 = no mention, 1 = mention only, 2 = learning outcome, 3 = taught content, 4 = assessed) and scope (technical, clinical, ethical, research), as well as stated future intent. Interventional radiology (IR) curricula were reviewed separately. Results: AI was mentioned in 7/11 (64%) curricula; three (UK, Australia/New Zealand, and Ireland) reached Depth 3; others ranged between 1 and 2. All seven included technical content, four covered clinical applications, and only two addressed ethics. Only 2/11 (18%) stated future intent to expand AI teaching. VR was mentioned in 5/11 (46%) curricula, with only one reaching Depth 2. Six out of the 11 curricula we reviewed had a separate curriculum for IR. Out of those, only one (ESR curriculum) mentioned VR, and none mentioned AI. Conclusion: Formal inclusion of AI and VR in postgraduate radiology curricula varies significantly across countries, with notable differences in depth and scope. VR is even less embedded, with few clear expansion plans. Adjustments to the curriculum are essential to prepare radiologists for the ongoing demands of modern technologies. Introduction Artificial intelligence (AI) and virtual reality (VR) are rapidly transforming healthcare and clinical radiology amongst the specialties already implementing their use, with applications ranging from prioritising urgent scans to improving image quality and support early disease detection (van Kooten et al., 2024 ). More than 1000 AI algorithms are now approved for medical imaging tasks; this shows the importance of training programmes to adapt these to their curricula to prepare radiologists to not only use these algorithms but also to critically evaluate and safely integrate them into practice (Venugopal et al., 2025 ). Trainees should have formal training in foundational AI competence and understand the capabilities and limitations to ensure safe and ethical patient care. This is supported by evidence of positive attitudes among radiology trainees towards the inclusion of AI in radiology. However, achieving this may be limited by barriers such as uneven access to resources and a lack of formalisation (Tejani et al., 2022 ; Garin et al., 2023 ). On the other hand, VR has recently become a popular tool in medical education and radiology training. Its immersive environment allows effective anatomy teaching and interactive procedural simulation, potentially exceeding the effectiveness of traditional methods (Chytas et al., 2021; Gamba & Hartery, 2024 ). In residency programs, VR offers opportunities for case exposure, collaborative learning, and remote training. It also has a positive impact in clinical practice such as surgical planning and teleradiology (Chytas, 2021; Gamba & Hartey, 2024). Despite its potential, the integration of VR into structured radiology training remains limited and uneven across institutions. As AI and VR increasingly shape the future of radiology, preparing the next generation of radiologists to engage critically and effectively with these technologies is essential. Despite growing recognition of the importance of AI and VR, the extent to which these technologies are incorporated into formal postgraduate training programs remains unclear. To address this gap, we conducted a cross-national analysis to evaluate the presence, depth, and scope of AI and VR education within official postgraduate radiology curricula. Methods Study design We conducted a structured document analysis of official postgraduate radiology training curricula. The primary objective was to evaluate the explicit integration of artificial intelligence (AI) and virtual reality (VR) within national and regional curricula, with a focus on both diagnostic and interventional radiology (IR). Data sources Between 15 July - 15 August 2025, publicly available curricula were identified through national training authority websites and professional society repositories. Eleven curricula were included, representing 16 countries across diverse geographic and economic contexts. These included the United Kingdom (UK), United States of America (USA), Canada, Nigeria, Australia and New Zealand, Germany, France, Malaysia, Kuwait, Ireland, and shared frameworks including the European Society of Radiology (ESR) curriculum (covering Austria, Switzerland, and other European countries) and the Royal Australian and New Zealand College of Radiologists (RANZCR) curriculum. Curricula that were incomplete, inaccessible, or not available in English were excluded. Data extraction All documents were reviewed using a structured proforma developed for this study. The proforma included: Rating the Depth of AI/VR Integration in Radiology Curricula (Table 1). This table outlines the 5-point ordinal scale used to classify the depth of artificial intelligence (AI) and virtual reality (VR) integration within postgraduate radiology training curricula. Scope of AI/VR Curriculum Coverage Across Technical, Clinical, Ethical, and Research Domains (Table 2). This table defines the four domains used to categorise the scope of AI and VR curriculum content Results International Comparison of Artificial Intelligence and Virtual Reality Integration in Postgraduate Radiology Training Curricula (Table 3). Comparison of AI and VR inclusion across national and regional postgraduate radiology curricula, including curriculum year, depth of AI/VR coverage, scope, and any stated future development plans. AI was mentioned in 7/11 (64%) curricula; three (UK, Australia/New Zealand, and Ireland) reached Depth 3, while the others ranged between 1 and 2. All seven included technical content, four covered clinical applications, and only two addressed ethics. Only 2/11 (18%) stated future intent to expand AI teaching. VR was mentioned in 5/11 (46%) curricula, with only one reaching Depth 2. Six out of the 11 curricula we reviewed had a separate curriculum for IR. Out of those, only one (ESR curriculum) mentioned VR, and none mentioned AI. Discussion AI Integration: Promise Without Structure AI is increasingly used in clinical radiology; however, our analysis reveals that its integration into postgraduate education remains limited and uneven. Two-thirds of curricula acknowledged AI, but only three (UK, Australia/New Zealand, and Ireland) progressed to structured teaching, and none included formal assessment. Where AI was included, it was primarily technical, with minimal inclusion of formal assessments and reference to ethical concerns. Without these aspects, there is a risk of producing radiologists who are familiar with AI concepts but lack the competence to implement these systems critically (Li et al., 2025 ). Trainee perspectives from different regions support the existence of this curricular gap. A national survey from Türkiye reported high awareness of AI among residents but almost no structured exposure during residency (Emekli et al., 2024 ). Similar findings have been reported in Europe and North America, where most trainees regard AI education as important but describe limited access to training opportunities (Muehlematter et al., 2021 ). In the United States, over 80% of residents agreed that AI should be integrated into residency programmes, yet only around one in five reported access to such teaching (Salastekar et al., 2023 ). A recent multicentre study found that only 12% of faculty radiologists, 3% of residents, and 7% of students were familiar with the implementation of AI in radiology education, with most participants having limited knowledge or lacking a fundamental understanding of AI in this context (Li et al., 2025 ). These findings show that there is a positive trainee perspective regarding the implementation of AI, but structured training remains limited. Several systemic factors help explain this gap. Curricula are revised on multi-year cycles, while AI tools develop within months (Li et al., 2025 ). Developing educational content requires annotated datasets, interactive modules, and clinical case material, as well as collaboration between radiologists, computer scientists, and educators, whose expertise remains limited (Gong & Patlas, 2023 ). Many departments also lack faculty who are comfortable teaching the topic. Combined with the resource demands of continuously updating content, these challenges may help explain why most national curricula mention AI in theory but do not yet provide structured, practical, and widely accessible training. The benefits of training radiologists to use AI is already evident. For example, van Kooten et al. ( 2024 ) implemented a 3-day AI curriculum in a radiology residency program using a framework that included forming an AI expert team, assessing resident knowledge and defining learning objectives. Their results demonstrated increased participant confidence in handling AI-based approaches, with lectures and expert-led discussions rated as most valuable. This shows that even brief, structured interventions can effectively improve AI knowledge and motivate other residency programs to include AI education into their curriculum. In summary, AI education should be embedded in the postgraduate radiology training curriculum to ensure that future radiologists not only understand AI but are able to apply it safely in clinical practice. Virtual Reality in Postgraduate Radiology Education: Discussion Our review shows that VR is underrepresented in postgraduate radiology curricula. Only 5 of 11 programs (46%) mentioned VR, and just one reached Depth 2. Most curricula offered only brief references, without defined learning outcomes, structured teaching modules, or formal assessment. This limited integration restricts trainees’ opportunities for immersive, hands-on learning. VR has been shown to improve anatomy comprehension, procedural simulation, and technical skill, while improving trainee confidence and spatial understanding of complex structures (Shetty et al., 2024 ; Lang et al., 2024 ). Multiple factors may explain the limited use of VR. High implementation costs, including investment in VR headsets, simulators, and software, are a significant barrier (Walsh et al., 2020 ; Najjar, 2023 ). Faculty expertise is another limitation, as teaching effectively with VR requires familiarity with simulation design and integration into clinical training. Continuous content updates are also necessary to align VR modules with the updating clinical guidelines, procedural techniques, and technology improvements, which require ongoing institutional support and resources. These challenges are more evident in lower-resource settings, where access to hardware and IT infrastructure may be limited. Despite these barriers, VR has demonstrated clear educational benefits. Simulation allows trainees to practice complex procedures in a safe, repeatable environment without patient risk (Kotwal et al., 2021 ). It has also been shown to improve procedural competence, reduce error rates, and allows for a smoother transfer of skills to clinical practice (Alaker et al., 2016 ; Shetty et al., 2024 ). In summary, the structured inclusion of VR in the postgraduate training curriculum helps future radiologists develop their skills and confidence safely and effectively before applying them in clinical practice. Interventional radiology: a unique gap Our review shows a significant gap in the inclusion of AI and VR in interventional radiology (IR) curricula. Of the six countries that had separate IR curricula, only the ESR curriculum referenced VR, and none included AI. This is surprising as IR is among the subspecialties most suited to benefit from both simulation-based training and AI-assisted procedural planning. IR procedures are technically demanding, often involve steep learning curves, and carry inherent risks to patients. Recent studies show the potential of VR in interventional radiology. For instance, Gelmini et al. ( 2021 ) demonstrated VR's effectiveness in skill acquisition and learning in IR. Similarly, Guo et al. ( 2020 ) proposed a VR-based radiation safety training system aimed at minimising occupational radiation exposure. AI has the potential to support case selection, imaging guidance, and intra-procedural decision-making, yet remains absent from formal curricula. Leaving VR and AI outside of structured IR education results in trainee experience being heavily dependent on local institutional resources. Evidence from other surgical and procedural specialties indicates that integrating simulation into training not only improves technical skills but also the confidence and reduces error rates (Shahrezaei et al., 2024 ). To ensure trainees are prepared for the developing technologies, national curricula should incorporate AI and VR as structured and assessed components of IR training. Integrating these formally would standardise the access, reduce disparities between institutions, and prepare the future interventional radiologists to safely and effectively lead in a technology-enabled era of minimally invasive care. Global inequalities in AI and VR integration The integration of AI and VR into the curricula risks increasing the disparities between high- and low-resource settings. In high-income countries, trainees increasingly benefit from structured exposure to advanced digital tools, supported by institutional investment, dedicated infrastructure, and access to specialist faculty. In contrast, trainees in low- and middle-income countries often rely on ad hoc or locally developed initiatives that may not align with international standards. This risks creating a two-tiered workforce: one proficient in emerging technologies and prepared for the evolving demands of radiology, and another that is underprepared to engage with the digital transformation of the practice. Li et al. ( 2024 ) systematically reviewed 17 studies from LMICs and found that learners generally reported positive experiences with VR; however, accessibility issues, high costs, device availability, and inadequate infrastructure limited its implementation. Simulation-based training faces similar inequities more broadly; although well established in high-income settings, its uptake in LMICs continues to be restricted by resource and infrastructural constraints. Parallel barriers exist in the adoption of AI in radiology education and practice. Soreta et al. ( 2025 ) and Malloura (2020) reported that the integration of AI in LMICs is hindered by insufficient IT infrastructure, poor connectivity, and shortages in faculty members that would provide the training. Limitations Several limitations should be acknowledged. First, this review was restricted to curriculum documents that were publicly available online and published in English. As a result, some countries with formal training structures may have been excluded if their curricula were not accessible through open sources. Similarly, informal or locally delivered initiatives, particularly in low- and middle-income countries (LMICs), are unlikely to have been captured. This reliance on online access introduces an availability bias that may underestimate the true extent of AI and VR teaching worldwide. Secondly, we focused on formal, national curricula, which may not always reflect how training is actually delivered in practice. The presence of content in a curriculum does not guarantee equal delivery across institutions. Similarly, the absence of its mention does not necessarily mean that relevant teaching never occurs. Individual programs may adopt teaching approaches that remain undocumented at the national level, and our methodology could not capture these. Finally, the study represents a cross-sectional snapshot of curricula at a single point in time. While this provides a valuable benchmark, curricula are dynamic documents and may evolve in the future as professional bodies respond to the growing importance of these technologies. Our findings should therefore be reflective of current commitments rather than permanent positions. Despite these limitations, the use of a systematic search strategy and a structured proforma strengthens the validity of the analysis. It provides a comprehensive overview of formal, national-level commitments to integrating AI and VR in radiology training. By highlighting both the presence and absence of explicit references, this work offers a valuable foundation for understanding current trends and identifying opportunities for curriculum development. Towards a Future-Ready Curriculum Our findings support the structured integration of AI and VR into radiology training curriculum. To move beyond aspirational or superficial mentions, curricula should adopt a systematic approach that: Embeds AI and VR across multiple domains, including technical, clinical, ethical, and research, ensuring that future radiologists are able to safely use these technologies in patient care. Introduces progressive depth of training, beginning with early awareness during undergraduate or foundational stages and advancing towards assessed competence at higher levels of speciality training. Provides explicit coverage within interventional radiology curricula, where simulation-based training and AI-driven procedural planning are particularly relevant, given the technical complexity and inherent risks associated with IR practice. Promotes global equity by aligning international frameworks (e.g., ESR, RANZCR) with strategies designed to support lower-resource settings, thereby reducing disparities in trainee exposure and ensuring that technological advances do not widen existing gaps in educational opportunity or patient care quality. Conclusion Our cross-national analysis shows that while AI and VR are rapidly advancing in clinical practice, their integration into the postgraduate training curricula remains limited and superficial. Interventional radiology, although ideally suited to simulation and AI support, is especially underrepresented in the curricula. To prepare radiologists for a digital future, training must go beyond aspirational mentions and embed AI and VR as structured, assessed components across technical, clinical, ethical, and research domains. Standardised, competency-based approaches, combined with international efforts to standardise access and reduce inequities, will ensure that radiologists can use these tools safely and lead innovation in medical imaging. Declarations Funding: This study was not supported by any funding. Conflict of interest: The authors declare that they have no conflict of interest. Ethical approval: For this type of study, no ethical approval or formal consent is required. No animal studies were performed. Informed consent : For this type of study, informed consent is not required. Consent for publication: For this type of study, consent for publication is not required. Availability of data and materials: Not applicable. Competing interests: All authors declare that they have no competing interests. Acknowledgement: Not applicable Authors’ Contributions: 1. Dr Mohammed Bilal: Led the conception and design of the study, coordinated data collection, performed the primary data analysis, and drafted the initial version of the manuscript. 2. Dr Rayhan Yousef Gasiea: Contributed to study design, assisted with data acquisition, supported data analysis, and provided substantial revisions to the manuscript. 3. Dr Abdual Rahman Alvi: Conducted the literature review, contributed to data interpretation, and reviewed the manuscript for important intellectual content. 4. Professor Mohamad Hamady: Provided expert supervision throughout the project, contributed to methodological guidance, assisted with interpretation of findings, and critically revised the manuscript. 5. Dr Bella Huasen: Supported data interpretation, contributed to manuscript refinement, and reviewed the final version for accuracy and intellectual content. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. References van Kooten MJ, Tan CO, Hofmeijer EIS, van Ooijen PMA, Noordzij W, Lamers MJ, Kwee TC, Vliegenthart R, Yakar D. A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist. Insights into imaging. 2024;15(1):15. https://doi.org/10.1186/s13244-023-01595-3 . Venugopal VK, Kumar A, Tan MO, Szarf G. Curriculum check, 2025-equipping radiology residents for AI challenges of tomorrow. Abdom Radiol (New York). 2025. 10.1007/s00261-025-05016-5 . Advance online publication. Tejani AS, Elhalawani H, Moy L, Kohli M, Kahn CE Jr. Artificial Intelligence and Radiology Education. Radiol Artif Intell. 2022;5(1):e220084. https://doi.org/10.1148/ryai.220084 . 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Bridging the AI gap in clinical imaging: Opportunities and strategies for low- and middle-income countries. J Global Radiol. 2025;11(2). https://doi.org/10.7191/jgr.985 . Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 05 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 23 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8185340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555696331,"identity":"222eeb5e-5b85-4c16-a14f-7849022ae1e2","order_by":0,"name":"Mohammed 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09:42:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2442603,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8185340/v1/85d1e8ae20a72205547374df.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking Digital Future of Education: A Global Review of AI and VR Education in Diagnostic and Interventional Radiology Postgraduate Training","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) and virtual reality (VR) are rapidly transforming healthcare and clinical radiology amongst the specialties already implementing their use, with applications ranging from prioritising urgent scans to improving image quality and support early disease detection (van Kooten et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More than 1000 AI algorithms are now approved for medical imaging tasks; this shows the importance of training programmes to adapt these to their curricula to prepare radiologists to not only use these algorithms but also to critically evaluate and safely integrate them into practice (Venugopal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Trainees should have formal training in foundational AI competence and understand the capabilities and limitations to ensure safe and ethical patient care. This is supported by evidence of positive attitudes among radiology trainees towards the inclusion of AI in radiology. However, achieving this may be limited by barriers such as uneven access to resources and a lack of formalisation (Tejani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, VR has recently become a popular tool in medical education and radiology training. Its immersive environment allows effective anatomy teaching and interactive procedural simulation, potentially exceeding the effectiveness of traditional methods (Chytas et al., 2021; Gamba \u0026amp; Hartery, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In residency programs, VR offers opportunities for case exposure, collaborative learning, and remote training. It also has a positive impact in clinical practice such as surgical planning and teleradiology (Chytas, 2021; Gamba \u0026amp; Hartey, 2024). Despite its potential, the integration of VR into structured radiology training remains limited and uneven across institutions.\u003c/p\u003e\u003cp\u003eAs AI and VR increasingly shape the future of radiology, preparing the next generation of radiologists to engage critically and effectively with these technologies is essential. Despite growing recognition of the importance of AI and VR, the extent to which these technologies are incorporated into formal postgraduate training programs remains unclear. To address this gap, we conducted a cross-national analysis to evaluate the presence, depth, and scope of AI and VR education within official postgraduate radiology curricula.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a structured document analysis of official postgraduate radiology training curricula. The primary objective was to evaluate the explicit integration of artificial intelligence (AI) and virtual reality (VR) within national and regional curricula, with a focus on both diagnostic and interventional radiology (IR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 15 July - 15 August 2025, publicly available curricula were identified through national training authority websites and professional society repositories. Eleven curricula were included, representing 16 countries across diverse geographic and economic contexts. These included the United Kingdom (UK), United States of America (USA), Canada, Nigeria, Australia and New Zealand, Germany, France, Malaysia, Kuwait, Ireland, and shared frameworks including the European Society of Radiology (ESR) curriculum (covering Austria, Switzerland, and other European countries) and the Royal Australian and New Zealand College of Radiologists (RANZCR) curriculum. Curricula that were incomplete, inaccessible, or not available in English were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll documents were reviewed using a structured proforma developed for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proforma included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eRating the Depth of AI/VR Integration in Radiology Curricula (Table 1). This table outlines the 5-point ordinal scale used to classify the depth of artificial intelligence (AI) and virtual reality (VR) integration within postgraduate radiology training curricula.\u003c/li\u003e\n \u003cli\u003eScope of AI/VR Curriculum Coverage Across Technical, Clinical, Ethical, and Research Domains (Table 2). This table defines the four domains used to categorise the scope of AI and VR curriculum content\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Results","content":"\u003cp\u003eInternational Comparison of Artificial Intelligence and Virtual Reality Integration in Postgraduate Radiology Training Curricula (Table 3). Comparison of AI and VR inclusion across national and regional postgraduate radiology curricula, including curriculum year, depth of AI/VR coverage, scope, and any stated future development plans.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI was mentioned in 7/11 (64%) curricula; three (UK, Australia/New Zealand, and Ireland) reached Depth 3, while the others ranged between 1 and 2. All seven included technical content, four covered clinical applications, and only two addressed ethics. Only 2/11 (18%) stated future intent to expand AI teaching. VR was mentioned in 5/11 (46%) curricula, with only one reaching Depth 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSix out of the 11 curricula we reviewed had a separate curriculum for IR. Out of those, only one (ESR curriculum) mentioned VR, and none mentioned AI.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eAI Integration: Promise Without Structure\u003c/h2\u003e\u003cp\u003eAI is increasingly used in clinical radiology; however, our analysis reveals that its integration into postgraduate education remains limited and uneven. Two-thirds of curricula acknowledged AI, but only three (UK, Australia/New Zealand, and Ireland) progressed to structured teaching, and none included formal assessment. Where AI was included, it was primarily technical, with minimal inclusion of formal assessments and reference to ethical concerns. Without these aspects, there is a risk of producing radiologists who are familiar with AI concepts but lack the competence to implement these systems critically (Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTrainee perspectives from different regions support the existence of this curricular gap. A national survey from T\u0026uuml;rkiye reported high awareness of AI among residents but almost no structured exposure during residency (Emekli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar findings have been reported in Europe and North America, where most trainees regard AI education as important but describe limited access to training opportunities (Muehlematter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the United States, over 80% of residents agreed that AI should be integrated into residency programmes, yet only around one in five reported access to such teaching (Salastekar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A recent multicentre study found that only 12% of faculty radiologists, 3% of residents, and 7% of students were familiar with the implementation of AI in radiology education, with most participants having limited knowledge or lacking a fundamental understanding of AI in this context (Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings show that there is a positive trainee perspective regarding the implementation of AI, but structured training remains limited.\u003c/p\u003e\u003cp\u003eSeveral systemic factors help explain this gap. Curricula are revised on multi-year cycles, while AI tools develop within months (Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Developing educational content requires annotated datasets, interactive modules, and clinical case material, as well as collaboration between radiologists, computer scientists, and educators, whose expertise remains limited (Gong \u0026amp; Patlas, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many departments also lack faculty who are comfortable teaching the topic. Combined with the resource demands of continuously updating content, these challenges may help explain why most national curricula mention AI in theory but do not yet provide structured, practical, and widely accessible training.\u003c/p\u003e\u003cp\u003eThe benefits of training radiologists to use AI is already evident. For example, van Kooten et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) implemented a 3-day AI curriculum in a radiology residency program using a framework that included forming an AI expert team, assessing resident knowledge and defining learning objectives. Their results demonstrated increased participant confidence in handling AI-based approaches, with lectures and expert-led discussions rated as most valuable. This shows that even brief, structured interventions can effectively improve AI knowledge and motivate other residency programs to include AI education into their curriculum.\u003c/p\u003e\u003cp\u003eIn summary, AI education should be embedded in the postgraduate radiology training curriculum to ensure that future radiologists not only understand AI but are able to apply it safely in clinical practice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eVirtual Reality in Postgraduate Radiology Education: Discussion\u003c/h2\u003e\u003cp\u003eOur review shows that VR is underrepresented in postgraduate radiology curricula. Only 5 of 11 programs (46%) mentioned VR, and just one reached Depth 2. Most curricula offered only brief references, without defined learning outcomes, structured teaching modules, or formal assessment. This limited integration restricts trainees\u0026rsquo; opportunities for immersive, hands-on learning. VR has been shown to improve anatomy comprehension, procedural simulation, and technical skill, while improving trainee confidence and spatial understanding of complex structures (Shetty et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple factors may explain the limited use of VR. High implementation costs, including investment in VR headsets, simulators, and software, are a significant barrier (Walsh et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Najjar, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Faculty expertise is another limitation, as teaching effectively with VR requires familiarity with simulation design and integration into clinical training. Continuous content updates are also necessary to align VR modules with the updating clinical guidelines, procedural techniques, and technology improvements, which require ongoing institutional support and resources. These challenges are more evident in lower-resource settings, where access to hardware and IT infrastructure may be limited.\u003c/p\u003e\u003cp\u003eDespite these barriers, VR has demonstrated clear educational benefits. Simulation allows trainees to practice complex procedures in a safe, repeatable environment without patient risk (Kotwal et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has also been shown to improve procedural competence, reduce error rates, and allows for a smoother transfer of skills to clinical practice (Alaker et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shetty et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summary, the structured inclusion of VR in the postgraduate training curriculum helps future radiologists develop their skills and confidence safely and effectively before applying them in clinical practice.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInterventional radiology: a unique gap\u003c/h3\u003e\n\u003cp\u003eOur review shows a significant gap in the inclusion of AI and VR in interventional radiology (IR) curricula. Of the six countries that had separate IR curricula, only the ESR curriculum referenced VR, and none included AI. This is surprising as IR is among the subspecialties most suited to benefit from both simulation-based training and AI-assisted procedural planning.\u003c/p\u003e\u003cp\u003eIR procedures are technically demanding, often involve steep learning curves, and carry inherent risks to patients. Recent studies show the potential of VR in interventional radiology. For instance, Gelmini et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated VR's effectiveness in skill acquisition and learning in IR. Similarly, Guo et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed a VR-based radiation safety training system aimed at minimising occupational radiation exposure. AI has the potential to support case selection, imaging guidance, and intra-procedural decision-making, yet remains absent from formal curricula.\u003c/p\u003e\u003cp\u003eLeaving VR and AI outside of structured IR education results in trainee experience being heavily dependent on local institutional resources. Evidence from other surgical and procedural specialties indicates that integrating simulation into training not only improves technical skills but also the confidence and reduces error rates (Shahrezaei et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo ensure trainees are prepared for the developing technologies, national curricula should incorporate AI and VR as structured and assessed components of IR training. Integrating these formally would standardise the access, reduce disparities between institutions, and prepare the future interventional radiologists to safely and effectively lead in a technology-enabled era of minimally invasive care.\u003c/p\u003e\n\u003ch3\u003eGlobal inequalities in AI and VR integration\u003c/h3\u003e\n\u003cp\u003eThe integration of AI and VR into the curricula risks increasing the disparities between high- and low-resource settings. In high-income countries, trainees increasingly benefit from structured exposure to advanced digital tools, supported by institutional investment, dedicated infrastructure, and access to specialist faculty. In contrast, trainees in low- and middle-income countries often rely on ad hoc or locally developed initiatives that may not align with international standards. This risks creating a two-tiered workforce: one proficient in emerging technologies and prepared for the evolving demands of radiology, and another that is underprepared to engage with the digital transformation of the practice.\u003c/p\u003e\u003cp\u003eLi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) systematically reviewed 17 studies from LMICs and found that learners generally reported positive experiences with VR; however, accessibility issues, high costs, device availability, and inadequate infrastructure limited its implementation. Simulation-based training faces similar inequities more broadly; although well established in high-income settings, its uptake in LMICs continues to be restricted by resource and infrastructural constraints.\u003c/p\u003e\u003cp\u003eParallel barriers exist in the adoption of AI in radiology education and practice. Soreta et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Malloura (2020) reported that the integration of AI in LMICs is hindered by insufficient IT infrastructure, poor connectivity, and shortages in faculty members that would provide the training.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, this review was restricted to curriculum documents that were publicly available online and published in English. As a result, some countries with formal training structures may have been excluded if their curricula were not accessible through open sources. Similarly, informal or locally delivered initiatives, particularly in low- and middle-income countries (LMICs), are unlikely to have been captured. This reliance on online access introduces an availability bias that may underestimate the true extent of AI and VR teaching worldwide.\u003c/p\u003e\u003cp\u003eSecondly, we focused on formal, national curricula, which may not always reflect how training is actually delivered in practice. The presence of content in a curriculum does not guarantee equal delivery across institutions. Similarly, the absence of its mention does not necessarily mean that relevant teaching never occurs. Individual programs may adopt teaching approaches that remain undocumented at the national level, and our methodology could not capture these.\u003c/p\u003e\u003cp\u003eFinally, the study represents a cross-sectional snapshot of curricula at a single point in time. While this provides a valuable benchmark, curricula are dynamic documents and may evolve in the future as professional bodies respond to the growing importance of these technologies. Our findings should therefore be reflective of current commitments rather than permanent positions.\u003c/p\u003e\u003cp\u003eDespite these limitations, the use of a systematic search strategy and a structured proforma strengthens the validity of the analysis. It provides a comprehensive overview of formal, national-level commitments to integrating AI and VR in radiology training. By highlighting both the presence and absence of explicit references, this work offers a valuable foundation for understanding current trends and identifying opportunities for curriculum development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTowards a Future-Ready Curriculum\u003c/h2\u003e\u003cp\u003eOur findings support the structured integration of AI and VR into radiology training curriculum. To move beyond aspirational or superficial mentions, curricula should adopt a systematic approach that:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEmbeds AI and VR across multiple domains, including technical, clinical, ethical, and research, ensuring that future radiologists are able to safely use these technologies in patient care.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntroduces progressive depth of training, beginning with early awareness during undergraduate or foundational stages and advancing towards assessed competence at higher levels of speciality training.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eProvides explicit coverage within interventional radiology curricula, where simulation-based training and AI-driven procedural planning are particularly relevant, given the technical complexity and inherent risks associated with IR practice.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePromotes global equity by aligning international frameworks (e.g., ESR, RANZCR) with strategies designed to support lower-resource settings, thereby reducing disparities in trainee exposure and ensuring that technological advances do not widen existing gaps in educational opportunity or patient care quality.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur cross-national analysis shows that while AI and VR are rapidly advancing in clinical practice, their integration into the postgraduate training curricula remains limited and superficial. Interventional radiology, although ideally suited to simulation and AI support, is especially underrepresented in the curricula. To prepare radiologists for a digital future, training must go beyond aspirational mentions and embed AI and VR as structured, assessed components across technical, clinical, ethical, and research domains. Standardised, competency-based approaches, combined with international efforts to standardise access and reduce inequities, will ensure that radiologists can use these tools safely and lead innovation in medical imaging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was not supported by any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e For this type of study, no ethical approval or formal consent is required. No animal studies were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e: For this type of study, informed consent is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e For this type of study, consent for publication is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e All authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; \u0026nbsp; \u0026nbsp; Dr Mohammed Bilal:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLed the conception and design of the study, coordinated data collection, performed the primary data analysis, and drafted the initial version of the manuscript.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u0026nbsp; \u0026nbsp; Dr Rayhan Yousef Gasiea:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContributed to study design, assisted with data acquisition, supported data analysis, and provided substantial revisions to the manuscript.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u0026nbsp; \u0026nbsp; Dr Abdual Rahman Alvi:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConducted the literature review, contributed to data interpretation, and reviewed the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; \u0026nbsp; \u0026nbsp; Professor Mohamad Hamady:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProvided expert supervision throughout the project, contributed to methodological guidance, assisted with interpretation of findings, and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp; \u0026nbsp; Dr Bella Huasen:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupported data interpretation, contributed to manuscript refinement, and reviewed the final version for accuracy and intellectual content.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Kooten MJ, Tan CO, Hofmeijer EIS, van Ooijen PMA, Noordzij W, Lamers MJ, Kwee TC, Vliegenthart R, Yakar D. 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Radiology. 2020;297(3):513\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/radiol.2020201434\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2020201434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoreta YG, et al. Bridging the AI gap in clinical imaging: Opportunities and strategies for low- and middle-income countries. J Global Radiol. 2025;11(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7191/jgr.985\u003c/span\u003e\u003cspan address=\"10.7191/jgr.985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8185340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8185340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs artificial intelligence (AI) and virtual reality (VR) continue to expand in radiology, structured education is essential for preparing radiologists (Tejani et al., 2022). Despite calls for formal training, the extent to which these technologies are integrated into postgraduate curricula remains unclear. This study assessed the depth and scope of AI and VR education within official postgraduate radiology curricula.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed 11 official postgraduate radiology curricula from 16 countries across diverse economic and geographic contexts, including the UK, Malaysia, Nigeria, and shared frameworks such as the ESR curriculum (Austria, Switzerland, and others) and the RANZCR curriculum for Australia/New Zealand. Publicly available national training documents were reviewed. Data were extracted using a structured proforma that assessed AI and VR content by depth (0 = no mention, 1 = mention only, 2 = learning outcome, 3 = taught content, 4 = assessed) and scope (technical, clinical, ethical, research), as well as stated future intent. Interventional radiology (IR) curricula were reviewed separately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI was mentioned in 7/11 (64%) curricula; three (UK, Australia/New Zealand, and Ireland) reached Depth 3; others ranged between 1 and 2. All seven included technical content, four covered clinical applications, and only two addressed ethics. Only 2/11 (18%) stated future intent to expand AI teaching. VR was mentioned in 5/11 (46%) curricula, with only one reaching Depth 2.\u003c/p\u003e\n\u003cp\u003eSix out of the 11 curricula we reviewed had a separate curriculum for IR. Out of those, only one (ESR curriculum) mentioned VR, and none mentioned AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal inclusion of AI and VR in postgraduate radiology curricula varies significantly across countries, with notable differences in depth and scope. VR is even less embedded, with few clear expansion plans. Adjustments to the curriculum are essential to prepare radiologists for the ongoing demands of modern technologies.\u003c/p\u003e","manuscriptTitle":"Unlocking Digital Future of Education: A Global Review of AI and VR Education in Diagnostic and Interventional Radiology Postgraduate Training","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 08:28:16","doi":"10.21203/rs.3.rs-8185340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-12T05:03:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266199424278534813838535000458177795964","date":"2026-02-02T23:41:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T08:24:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T01:39:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T01:37:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-11-23T12:21:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f46fa94-867c-43cc-99b4-ce1682525e6f","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T08:28:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 08:28:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8185340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8185340","identity":"rs-8185340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}