The Use of Metaverse in Radiology: A Systematic Review.

Parastoo Amiri: conceptualization, writing – original draft, methodology, writing – review and editing, resources, investigation. Taleb Khodaveisi: conceptualization, validation, funding acquisition, investigation, writing – original draft. Nasim Aslani: conceptualization, funding acquisition, writing – original draft, methodology, writing – review and editing. Hadideh Ferdos: conceptualization, investigation. Reyhane Kornokar: writing – review and editing.

This manuscript does not include any experimental work with animals or human subjects. This research was supported by the Lorestan University of Medical Sciences (Code of ethics: IR. LUMS. REC.1403.137).

The authors received no specific funding for this work.

This review is reported according to the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) (Figure  1 ) [ 22 ]. Flow diagram of study selection. This study was a systematic review of the use of metaverse in radiology. Our search was limited to studies published until June 2024. We searched PubMed, Scopus, and Web of Science for relevant papers in English. A data extraction form, which was developed based on the research aim, was used for data collection. Lists of Medical Subject Headings (MESH) terms, keywords, as well as their synonyms were used for terms such as metaverse and radiology. Detailed search strategies for each database can be found in the Supporting file  1 . We included studies according to the following inclusion criteria: 1) all types of studies about metaverse applications and effects on radiology, and 2) the study was an original research article. We excluded studies according to the following exclusion criteria: aim of study is out of the scope of our review, articles about using metaverse in other medical fields other than radiology (e.g., surgical planning or physical rehabilitation), articles investigating virtual reality for general wellness without specific diagnostic or educational use in radiology, systematic reviews, commentaries, opinion papers, editorials, grey literature, preprints, news, and abstracts presented in a congress. Due to the diverse nature of the included studies, which encompass a range of designs such as case reports, experimental studies, prospective cohort studies, randomized controlled trials (RCTs), and developmental or applied research focused on novel metaverse technologies in radiology, a standardized quality appraisal was not feasible. Many of these studies, particularly those of developmental nature, lack established quality assessment tools (e.g., MMAT or GRADE), as such tools are primarily designed for clinical or interventional research and may not adequately evaluate technical or exploratory studies. Three authors screened titles and abstracts to find relevant articles based on our inclusion/exclusion criteria. Then, articles were selected for full‐text review. All studies were independently reviewed in detail by PA, NA, and TKh. Disagreements were resolved by consensus. Endnote version 20 was used to manage articles. A data list from each full‐text article was created in an Excel spreadsheet 2019. The data list included the following headings: authors (publication year), country, type of study, setting, hardware, software tools and algorithms, modality types, objectives, and outcomes.

Of the 212 studies identified through searches in reputable databases, 35 duplicates were removed, leaving 177 studies for title and abstract screening. During this stage, 139 studies were excluded due to their lack of relevance to metaverse applications in radiology. In the full‐text review stage, 38 studies were assessed for eligibility, of which 17 were excluded for the following reasons: lack of specific focus on metaverse applications in radiology ( n  = 13) and unavailability of full text or insufficient data ( n  = 4). Ultimately, 21 studies meeting the inclusion criteria, which directly addressed the use of metaverse in radiology, including improving diagnostic accuracy, medical education, treatment planning, and interventional applications, were included in this review (Figure  1 ). Remarkably, the trend demonstrated an upward trajectory over the years: while only one relevant article was identified in 2020, subsequent years witnessed an increase, with 2022 having five relevant studies, 2023 featuring ten, and 2024 contributing five. In this regard, Table  1 presents comprehensive information about the included studies, encompassing details such as types of study, research settings, hardware specifications, software or algorithms employed, and the specific types of modalities utilized. This detailed overview allows for a thorough understanding of the research landscape in the field. Furthermore, the studies have been categorized into four main groups [ 1 ]: applications in interventional radiology and surgical planning [ 2 ], medical education and user experiences [ 3 ], treatment planning, and [ 4 ] upscaling diagnostic capabilities. Most significant characteristics of included studies. − Display hardware (VR headsets or screens) − Computing hardware (high‐performance CPUs and GPUs) Display hardware (VR headsets or screens) Computing hardware (high‐performance CPUs and GPUs) − Cardiac Review 3D software − Unity Engine Cardiac Review 3D software Unity Engine − MRI MRI − Oculus Quest 2 HMD − Tablet Oculus Quest 2 HMD Tablet − Unity − SteamVR SDK (V1.14) Unity SteamVR SDK (V1.14) − CT CT − PreXion 3D scanner PreXion 3D scanner − e‐Vol DX CBCT software − AI‐related algorithms e‐Vol DX CBCT software AI‐related algorithms − CTs of endodontics CTs of endodontics − Laptop − VR Pen Laptop VR Pen − Unity − SteamVR SDK (V1.14) Unity SteamVR SDK (V1.14) − CT CT − N/M N/M − Automatic thresholding method − Marching Cubes algorithm − Tetrahedral mesh using Tetgen − MANO topology − Remeshing algorithm − Manual landmark labelling method − Iterative shape registration and parameter learning Automatic thresholding method Marching Cubes algorithm Tetrahedral mesh using Tetgen MANO topology Remeshing algorithm Manual landmark labelling method Iterative shape registration and parameter learning − 200 hand MRI volumes spanning 50 different hand postures of 35 individual subjects 200 hand MRI volumes spanning 50 different hand postures of 35 individual subjects − Echocardiogram − Dataset server − Clinicians terminals − Edge server Echocardiogram Dataset server Clinicians terminals Edge server − Multi‐Scale Gated Axial‐Transformer Network (MSGATNet) − Semantic Parsing Network (SPReCHD) Multi‐Scale Gated Axial‐Transformer Network (MSGATNet) Semantic Parsing Network (SPReCHD) − 2100 fetal US FC views from 2500 fetuses from 24 to 26 weeks of gestation − Echocardiography 2100 fetal US FC views from 2500 fetuses from 24 to 26 weeks of gestation Echocardiography − HMD equipped with eye, hand and object‐tracking sensors − Headset with the VIVE Focus 3 Eye Tracker − Rechargeable headset batteries HMD equipped with eye, hand and object‐tracking sensors Headset with the VIVE Focus 3 Eye Tracker Rechargeable headset batteries − Unity − Teleportation − Mobile MRI 3D model Unity Teleportation Mobile MRI 3D model − MRI MRI − HapLeap − Oculus Quest HMD HapLeap Oculus Quest HMD − Unity − Blender software Unity Blender software − CTs of lung CTs of lung − Oculus Quest 2 HMD − GSR electrodes Oculus Quest 2 HMD GSR electrodes − MATLAB programming language − SimNIBS v3.2 − NUL‐217 − GraphPad Prism − SPSS software MATLAB programming language SimNIBS v3.2 NUL‐217 GraphPad Prism SPSS software − 37 MRI of healthy subjects 37 MRI of healthy subjects − Oculus Quest 2 HMD Oculus Quest 2 HMD − Elucis VR platform Elucis VR platform − One MRI of uterus One MRI of uterus − Microsoft HoloLens − Multitude of sensors − Infrared Time‐of‐Flight depth camera − RGB camera − Four grayscale cameras − 12th Gen Intel Core i9‐12900K − 32GB RAM − Nvidia GeForce RTX 3090 − Azure Kinect Body Pose Tracking SDK version 1.1.2 Microsoft HoloLens Multitude of sensors Infrared Time‐of‐Flight depth camera RGB camera Four grayscale cameras 12th Gen Intel Core i9‐12900K 32GB RAM Nvidia GeForce RTX 3090 Azure Kinect Body Pose Tracking SDK version 1.1.2 − Deep Neural Net (DNN) Deep Neural Net (DNN) − Images taken of the human body using time‐of‐flight infrared depth cameras, RGB cameras, and grayscale cameras Images taken of the human body using time‐of‐flight infrared depth cameras, RGB cameras, and grayscale cameras − Medical image acquisition system Medical image acquisition system − Patch Partition and Feature Maps − Stages and Patch Merging − Swin Transformer Block Module − Multilayer Perceptron (MLP) − Window Multi‐Head Self‐Attention (W‐MSA) − Shifted Window‐Based Multi‐Head Self‐Attention (SW‐MSA) − Layer Normalization (LN) − DropPath − Self‐Attention Mechanism − Mean Hashing Algorithm − Logistic Chaotic Encryption Algorithm Patch Partition and Feature Maps Stages and Patch Merging Swin Transformer Block Module Multilayer Perceptron (MLP) Window Multi‐Head Self‐Attention (W‐MSA) Shifted Window‐Based Multi‐Head Self‐Attention (SW‐MSA) Layer Normalization (LN) DropPath Self‐Attention Mechanism Mean Hashing Algorithm Logistic Chaotic Encryption Algorithm − The choice of image type would likely depend on the specific healthcare data being secured in the metaverse The choice of image type would likely depend on the specific healthcare data being secured in the metaverse − MRI machine − CT machine − VR headset MRI machine CT machine VR headset − Self‐supervised sparse coding method, named the weighted iterative shrinkage thresholding algorithm (WISTA) − unfold the WISTA to construct a deep neural network (DNN) structured WISTA, named WISTA‐Net Self‐supervised sparse coding method, named the weighted iterative shrinkage thresholding algorithm (WISTA) unfold the WISTA to construct a deep neural network (DNN) structured WISTA, named WISTA‐Net − 50 MRI of the brain − 349 CT of the lungs 50 MRI of the brain 349 CT of the lungs − N/M N/M − Generative Adversarial Networks (GANs) − Residual Networks (ResNet) − Spike Neural Networks (SNN) − Spike Learning Technique − Python and PyTorch framework Generative Adversarial Networks (GANs) Residual Networks (ResNet) Spike Neural Networks (SNN) Spike Learning Technique Python and PyTorch framework − Kaggle dataset having 155 and 98 images for brain tumor and normal categories − GitHub dataset that comprise 3264 images with four categories, i.e., pituitary, glioma, meningioma tumor, and no tumor − publicly available dataset − having 3064 images with three categories, i.e., pituitary, glioma, and meningioma tumor from 233 patients Kaggle dataset having 155 and 98 images for brain tumor and normal categories GitHub dataset that comprise 3264 images with four categories, i.e., pituitary, glioma, meningioma tumor, and no tumor publicly available dataset having 3064 images with three categories, i.e., pituitary, glioma, and meningioma tumor from 233 patients − N/M N/M − Mean Squared Error (MSE) − Peak Signal‐to‐Noise Ratio (PSNR) − Structural Similarity Index Measure (SSIM) − Mean Method − Select Maximum Procedure − Choose the Minimalist Approach − Intensity Hue Saturation Fusion Method Mean Squared Error (MSE) Peak Signal‐to‐Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) Mean Method Select Maximum Procedure Choose the Minimalist Approach Intensity Hue Saturation Fusion Method − MRI and CT MRI and CT − N/M N/M − N/M N/M − Any type of medical images Any type of medical images − HMD HMD − A China United Imaging 1.5 T uMR660 MRI A China United Imaging 1.5 T uMR660 MRI − MRI MRI − GPU GPU − SolidWorks − CATIA − Rhino SolidWorks CATIA Rhino − CT CT − Headset Headset − Venlo − Limburg − The Netherlands Venlo Limburg The Netherlands − Cardiac Magnetic Resonance (CMR) Cardiac Magnetic Resonance (CMR) − Dataset − HG Dataset HG − Artiness Artiness − CT CT − Central processing unit (CPU) − random access memory (RAM) − HMD or HHD Central processing unit (CPU) random access memory (RAM) HMD or HHD − XR‐Software − XR client software XR‐Software XR client software − XR XR Abbreviations: CBCT, cone‐beam computed tomography; CS, cybersickness; GPU, graphics processing unit; GSR, galvanic skin response; HCMMNet, hierarchical conv‐MLP‐mixed network; HG, hologram; HMD, head‐mounted display; MRI, magnetic resonance imaging; N/M, not mentioned; NUL‐217, Neulog GSR logger sensor device; RCT, randomized clinical trials; UK, united kingdom; USA, united states of america; VR, virtual reality; TACS, transcranial alternating current stimulation. In Table  1 , we outline the key features of our research, which encompassed various study types. Among the included studies, developmental research was the most prevalent (52%), followed by experimental studies (19%) and applied research (14%). Additionally, we observed randomized controlled trials (5%), case reports (5%), and prospective cohort studies (5%), each contributing one study. The research settings exhibited diversity, with field settings hosting the majority of studies (57%), followed by controlled environments (33%). Notably, two studies (10%) did not explicitly specify their research environment. Furthermore, our investigation utilized a wide range of imaging modalities, including MRI, CT, echocardiography, and extended reality (XR), with MRI and CT being the most frequently employed techniques. Researchers leveraged hardware, specialized software, and advanced algorithms to explore the application of the metaverse in radiology research. In terms of categorization, the distribution of studies highlights a significant focus on treatment planning and diagnostic capabilities, with nine studies falling under category 4, indicating a strong emphasis on enhancing diagnostic accuracy and clinical outcomes. Additionally, the presence of six studies in category 2 suggests a robust interest in medical education and user experiences, reflecting the importance of training and interactive learning in the context of advanced imaging technologies. The relatively lower numbers in categories 1 and 3, with three studies each, suggest that while applications in interventional radiology and surgical planning are important, they are less represented in the current body of research compared to the other categories. This distribution underscores the evolving landscape of radiology research, where the integration of innovative technologies is driving advancements in both diagnostic capabilities and educational frameworks. The distribution of research articles by country is depicted in Figure  2 . China leads with the highest proportion of studies (33%), followed closely by the United States (USA) (23%) and Italy (14%). Additionally, the UK, Algeria, Canada, Brazil, India, and Korea each contribute 5% to the research landscape. Distribution of articles over different countries. Our systematic literature review aims to comprehensively analyze existing research on the utilization of the metaverse in radiology. Identifying the objectives and implications of these studies constitutes key findings that inform future research. In this context, we present the purposes and outcomes of the studies in Table  2 . Purposes and outcomes of the included studies. − Improved Diagnostic Accuracies in complex anatomical abnormalities − The full‐immersive VR was ranked as the most preferred display for performing group CHD discussions Improved Diagnostic Accuracies in complex anatomical abnormalities The full‐immersive VR was ranked as the most preferred display for performing group CHD discussions − Generalization of findings to other specialties − All‐round 3D contouring is promising. − Contour drawing processes are enhanced by using 3D immersive space Generalization of findings to other specialties All‐round 3D contouring is promising. Contour drawing processes are enhanced by using 3D immersive space − The method ensures 3D visualization of root canal slices and microanatomy, allowing dynamic navigation within the pulp cavity. − Computational modeling introduces new resources to Endodontics, potentially improving the predictability of root canal treatments. − Virtual visualization of the internal anatomy, replicating the canal, enhances communication, reliability, and clinical operationalization. − Root canal endoscopy using the novel CBCT filter can be applied clinically alongside innovative digital and virtual‐reality tools, aligning with Endodontics principles The method ensures 3D visualization of root canal slices and microanatomy, allowing dynamic navigation within the pulp cavity. Computational modeling introduces new resources to Endodontics, potentially improving the predictability of root canal treatments. Virtual visualization of the internal anatomy, replicating the canal, enhances communication, reliability, and clinical operationalization. Root canal endoscopy using the novel CBCT filter can be applied clinically alongside innovative digital and virtual‐reality tools, aligning with Endodontics principles − Improving interpretation of medical images − Practitioners who aim to integrate contour delineation processes into an immersive 3D space will benefit from the findings. Improving interpretation of medical images Practitioners who aim to integrate contour delineation processes into an immersive 3D space will benefit from the findings. − Providing a non‐rigid 3D hand model for Metaverse applications − Improved realism in Metaverse applications − Enhanced learning‐based hand pose and shape estimation Providing a non‐rigid 3D hand model for Metaverse applications Improved realism in Metaverse applications Enhanced learning‐based hand pose and shape estimation − Improving prenatal CHD detection rates − High performance in fetal CHD diagnosis − Improving the early diagnosis and treatment of CHD Improving prenatal CHD detection rates High performance in fetal CHD diagnosis Improving the early diagnosis and treatment of CHD − The success of the VR system indicates its effectiveness. − Attendees' willingness to use the VR system reflects user acceptance. − Users perceive it as a realistic approximation of a real‐life mobile MRI unit. − Increased interaction with non‐interactive VR equipment demonstrates versatility. − Providing an effective physical‐cyber interface enhances usability for displaying medical equipment. The success of the VR system indicates its effectiveness. Attendees' willingness to use the VR system reflects user acceptance. Users perceive it as a realistic approximation of a real‐life mobile MRI unit. Increased interaction with non‐interactive VR equipment demonstrates versatility. Providing an effective physical‐cyber interface enhances usability for displaying medical equipment. − The VR system effectively assists in tumor identification and measurement. − Testing virtual tumors in a VR application provides valuable insights. − Thermal feedback can enhance sensory experiences in healthcare. − The thermal model used is effective for tumor assessment. − Accurate tumor characteristic estimation is achievable. − Simulating “touching” virtual tumors in VR enhances user understanding. The VR system effectively assists in tumor identification and measurement. Testing virtual tumors in a VR application provides valuable insights. Thermal feedback can enhance sensory experiences in healthcare. The thermal model used is effective for tumor assessment. Accurate tumor characteristic estimation is achievable. Simulating “touching” virtual tumors in VR enhances user understanding. − The tACS significantly reduced CS nausea. − Improving level of nausea during the ride − Improving performance in regular VR users The tACS significantly reduced CS nausea. Improving level of nausea during the ride Improving performance in regular VR users − Metaverse‐Enhanced Imaging Assessment − Effective Postoperative Pain Management − Restored Bowel Function − Improved Urination Dysfunction Metaverse‐Enhanced Imaging Assessment Effective Postoperative Pain Management Restored Bowel Function Improved Urination Dysfunction − Lightweight networks like Blazepose and MoveNet were less accurate and achieved only 1‐2 FPS on the HoloLens 2 processor − Anatomical avatars overlaid on patients via HoloLens 2 could aid surgical planning, remote consultations, and medical training − Sensor improvements (higher resolution depth sensing, advanced motion tracking) would enhance accuracy Lightweight networks like Blazepose and MoveNet were less accurate and achieved only 1‐2 FPS on the HoloLens 2 processor Anatomical avatars overlaid on patients via HoloLens 2 could aid surgical planning, remote consultations, and medical training Sensor improvements (higher resolution depth sensing, advanced motion tracking) would enhance accuracy − Increasing the security of medical images − Improved resistance to geometric attacks such as rotation, scaling, clipping and translation − Eligibility for Metaverse Healthcare − Effective application in protecting the privacy and security of Metaverse healthcare images Increasing the security of medical images Improved resistance to geometric attacks such as rotation, scaling, clipping and translation Eligibility for Metaverse Healthcare Effective application in protecting the privacy and security of Metaverse healthcare images − Compared to the classic OMP and ISTA algorithms, WISTA‐Net performs better in removing image noise. − The average execution time of WISTA‐Net is also significantly shorter than that of the compared methods Compared to the classic OMP and ISTA algorithms, WISTA‐Net performs better in removing image noise. The average execution time of WISTA‐Net is also significantly shorter than that of the compared methods − The GASCNN effectively mitigates data and class leakage attacks, including minimum difference, leading bit, model inversion, and both transform‐based and direct class representation attacks. − The proposed work is about 52 times more energy‐efficient than standard ResNet. The GASCNN effectively mitigates data and class leakage attacks, including minimum difference, leading bit, model inversion, and both transform‐based and direct class representation attacks. The proposed work is about 52 times more energy‐efficient than standard ResNet. − The proposed fusion technique and quality criteria are valuable in medical image processing The proposed fusion technique and quality criteria are valuable in medical image processing − Improve image quality Improve image quality − Improved the clinical accuracy and safety of tumor surgery in non‐rigid bodies. Improved the clinical accuracy and safety of tumor surgery in non‐rigid bodies. − Improves the effectiveness of information expression − Improve transmission Improves the effectiveness of information expression Improve transmission − Reduce anxiety − Increase patient compliance − Improve the overall quality of patient care Reduce anxiety Increase patient compliance Improve the overall quality of patient care − The HGs major contribution was observed in the detection of branch arteries of upper lobes − The HGs were perceived to be easier to interpret than CT images The HGs major contribution was observed in the detection of branch arteries of upper lobes The HGs were perceived to be easier to interpret than CT images − Facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training Facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training As shown in Table  2 , these studies were conducted to address specific research questions or objectives. The goals across various areas encompassed assessing the appropriateness of technology in medicine, developing new diagnostic or therapeutic tools, integrating technologies for medical applications, advancing methodology and technique development in imaging, exploring clinical applications and impact studies, as well as innovating in modeling, simulation, and framework and platform development. Furthermore, reviewing the outcomes of studies revealed significant advancements in medical technology, encompassing the development of novel diagnostic tools, successful integration of imaging technologies, and innovative modeling and simulation approaches. These advancements contribute to enhancing clinical applications and patient care. Based on the distribution of studies across the four themes in Table  2 , the results reveal a clear emphasis on diagnostic imaging and AI integration, with 10 studies dedicated to this area. This predominance highlights the growing significance of artificial intelligence in advancing diagnostic accuracy and efficiency within medical imaging. Following this, surgical and interventional planning is the next most represented theme, with 5 studies, underscoring the critical role of imaging technologies in supporting preoperative and procedural decision‐making. Medical education and simulation, with 4 studies, reflects a notable interest in utilizing these technologies for training and educational purposes, which is essential for equipping future medical professionals with the necessary skills in an increasingly digital landscape. In contrast, patient‐centered design has the fewest studies, with only 2, suggesting that while important, it is less prioritized in the current body of research. This distribution indicates that the integration of innovative technologies is primarily driving advancements in diagnostic capabilities and procedural planning, alongside a recognition of the need for educational frameworks to keep pace with these developments. However, the limited focus on patient‐centered design points to a potential gap in research, where greater attention may be needed to ensure that technological innovations are effectively aligned with patient needs and experiences.

The present study systematically examines prior research on the use of the metaverse in the field of radiology. The findings show that the metaverse is creating many opportunities in the field of radiology. Most studies related to the application of the metaverse in radiology over the past 2 years have mainly involved assessing the appropriateness of technology in medicine, developing new diagnostic or therapeutic tools, integrating technologies for medical applications, advancing methodology and technique development in imaging, exploring clinical applications and impact studies, as well as innovating in modeling, simulation, and framework and platform development, which indicates an increase in the recognition and interest of radiology researchers and professionals in this technology. The variety of objectives arises from the diversity of research needs, and conflicting goals do not make sense in this context. The factor behind this issue is the nascent nature of the metaverse on the global stage. Park and Kim [ 44 ] emphasize that, given the broad scope of the metaverse technology′s objectives, it is essential for the developer community to classify, organize, and delineate topics effectively, with specialists in each field leading the efforts. Therefore, it is recommended to create an environment for utilizing metaverse technology to harness its numerous applications. Given the fundamental role of accurate diagnosis in patient treatment plans, the potential of the metaverse in complex and challenging diagnoses is significant [ 45 ]. The results of the present study indicate that the metaverse shows potential advantages over the traditional healthcare system in diagnosis and treatment. As an advanced technique in radiology, metaverses not only facilitate better diagnosis, but also significantly contribute to the overall improvement of disease management [ 46 ]. For example, a recent review study showed that the integration of innovative technologies such as VR, AR, and 3D imaging in the medical field is being used to improve the diagnosis and treatment of breast cancer and can improve the level of accuracy with minimal error and the efficiency of diagnostic and therapeutic methods [ 47 ]. Another review study has also shown that the metaverse offers promising opportunities for healthcare professionals to engage in immersive interactions with patients, which can potentially lead to better outcomes, reduced costs, and improved patient satisfaction [ 48 ]. Significant advances such as the development of new diagnostic tools and innovative modeling approaches demonstrate the potential of the metaverse to improve clinical applications and patient care [ 49 ]. However, the present review requires a deeper focus on patient‐centered aspects and clinical outcomes, which have typically received less attention in current research. The wide range of imaging methods used in the studies, including MRI, CT, echocardiography, and XR, emphasizes the versatility of the metaverse in enhancing various aspects of radiological practice. Kukla et al. [ 50 ], in their review, discussed the application of XR technologies, including VR and AR, in diagnostic imaging across various applications such as ultrasound, CT, and interventional radiology. In the present study, the included studies utilized a wide range of imaging methods, with MRI and CT being the most commonly used techniques. MRI and CT were the most common techniques, reflecting their primary role in diagnostic imaging and the potential of metaverse applications to enhance their effectiveness [ 51 ]. Metaverse technologies improve the skills and confidence of professionals and enhance radiology education [ 52 ]. In study [ 53 ], students preferred metaverse training and saw it as a valuable educational tool, which shows promise for the application of this technology in radiology education. While the use of the metaverse brings numerous benefits to patients, there are specific challenges, such as legal challenges in implementing metaverse projects, which require specific ethical and legal requirements for using the metaverse to provide diagnostic services [ 54 , 55 , 56 ]. The information collected by the metaverse can be used to shape consumer beliefs and behaviors, highlighting the need for transparent legal frameworks, especially for medical purposes [ 57 , 58 ]. A recent study by Benrimoh et al [ 59 ]. emphasized the importance of obtaining approval from medical regulatory systems such as HIPAA and PIPEDA before using metaverse technologies for medical purposes. HIPAA and PIPEDA are medical protocols created in the USA and Canada, respectively, to protect the privacy and integrity of health information. Metaverse systems may be vulnerable to cyber attacks and hacking, which requires decisive security measures to prevent theft and unauthorized access to patient information [ 60 ]. Another challenge of using the metaverse in healthcare is its complexity and high cost, as this technology in the healthcare sector is composed of various technologies such as blockchain, VR, AR, and AI, each of which requires high‐quality hardware and broad user adoption for implementation [ 61 , 62 ]. The variety of study types included in this review is noteworthy. Developmental research constitutes the majority (52%), with a strong emphasis on creating and refining metaverse applications tailored to radiology. This aligns with the findings of other studies that highlight the importance of innovation in medical technology [ 38 ]. Empirical studies (19%) and applied research (14%) contribute more to understanding practical applications, while the presence of randomized controlled trials, case reports, and prospective cohort studies (each at 5%) reflects a comprehensive methodological approach to examining the impact of the metaverse on radiology. Based on the research findings, China, the United States, and Italy have been leaders in studies related to the metaverse in recent years. The results of the present study align with the findings of Wang et al. [ 39 ], which indicate that countries such as China, the United States, and several European countries are leading the metaverse movement. It also aligns with the findings of Abbasi et al. [ 40 ], which state that China has been at the forefront of research related to the metaverse in recent years. However, it is somewhat inconsistent with the findings of López‐Belmonte [ 41 ], which highlight the United States as the leading country in the metaverse. Currently, research in this area is still in its early stages. The research settings differed significantly, with field settings comprising the majority of studies (57%). This indicates that many researchers are actively testing metaverse programs in real‐world environments, which is crucial for evaluating the practical implications of these technologies [ 42 ]. Controlled environments (33%) also play an important role, allowing for precise testing of hypotheses. However, the lack of specifications in two studies (10%) regarding their research environments indicates a potential area for improvement in future research reporting. Finally, to increase the effectiveness and applicability of the metaverse in radiology, it is necessary that these technologies be purposefully integrated into the short‐term and long‐term programs of health systems, with special attention given to patient‐centered and clinical outcome‐based approaches, ethics and data security, as well as educational and simulation capabilities. With emphasis on these aspects, it can be hoped that the metaverse will become one of the transformative technologies in radiologic care, leading to tangible improvements in therapeutic outcomes and patients' quality of life. One of the limitations of this study is that this scientific field is in the early stages of development, and many studies face limitations such as short follow‐up periods, small sample sizes, use of simulated environments instead of real clinical applications, and lack of RCTs. These limitations reduce the quality and validity of the existing evidence and highlight the necessity for future studies with more robust designs and longitudinal data. Also, another limitation of this study is the inaccessibility of articles published in languages other than English, as studies may have been published in different languages that researchers could not access, which is an inherent limitation of review studies. The absence of formal quality appraisal reflects a methodological challenge in synthesizing evidence from technically diverse, early‐phase studies. Future reviews should develop domain‐specific appraisal tools for metaverse‐technological research.

In light of the findings, the studies included in this research reveal significant advancements in medical technology within the field of radiology facilitated by the metaverse. These advancements encompass the development of new diagnostic tools, successful integration of imaging technologies, and innovative modeling and simulation approaches. However, as an emerging technology in the field of radiology, the metaverse is in the early stages of its growth and maturity, and it is expected that its applications in this field will increase in the coming years. Additionally, due to the limited number of articles on the use of the metaverse in radiology, it is not possible to fully and accurately measure its impacts in this area. To overcome this uncertainty, more studies need to be conducted in this field. As research in this field continues to expand, it is crucial for future studies to investigate the practical implications of these technologies, assess their impact on patient outcomes, and establish best practices for their seamless integration into clinical workflows. By doing so, the potential of the metaverse can be harnessed more effectively to revolutionize radiological practices and, ultimately, enhance patient care.

Medical imaging and radiology in diagnosing and treating diseases are considered primary and basic methods in medical science. These images include scans and radiology films taken by special and expensive medical devices. Benefiting from these images requires the skill and expertise of doctors in retrieving appropriate information for the implementation of diagnostic and treatment processes. Images allow doctors to examine the patient non‐invasively from multiple anatomical planes and make more appropriate decisions regarding their treatment [ 1 , 2 , 3 ]. In radiology, X‐ray images, MRI, CT scans, ultrasound, and mammography are used to examine organs and diagnose diseases, including cancer. Still, like many diagnostic methods, this method is imperfect and often subject to diagnostic uncertainty. There are many challenges in diagnosing diseases using medical images. One of these challenges is the false negative result, which means the patient is diagnosed as healthy despite having the disease [ 4 ]. The false positive result diagnoses the healthy person as sick. The presence of noise during image processing is one of the challenges faced by experts in this field. Radiology departments need diagnostic assistance methods to solve various challenges [ 1 ]. With the emergence of modern technologies and their diverse applications in medical sciences, imaging departments have also started using them to benefit as much as possible [ 5 ]. Artificial intelligence and technological platforms such as the Metaverse are among the technologies that can help in a more accurate and complete interpretation of medical images in different dimensions [ 4 ]. Metaverse is a hypothesis of the next generation of the Internet, which consists of 3D and decentralized virtual reality environments. This technology is a 3D digital space that connects the real environment with simulated ones using technologies such as virtual and augmented reality. This virtual world will be accessible through headsets, augmented reality glasses, smartphones, personal computers, and game consoles [ 6 , 7 , 8 ]. Reviews show that medical images include the most practical use of the Metaverse in medical sciences [ 6 ]. The Metaverse is an immersive digital environment powered by cutting‐edge technologies like virtual reality (VR), augmented reality (AR), artificial intelligence (AI), haptic feedback systems, and high‐speed 5 G networks. Designed to replicate real‐world interactions, it holds significant potential for revolutionizing healthcare. Applications span medical training, treatments, diagnostic procedures, medical imaging, telemedicine, and patient engagement. By delivering highly realistic and personalized experiences for both patients and practitioners, the metaverse enhances precision, reduces operational costs, and elevates care quality. As a transformative platform, it paves the way for a more efficient, accessible, and innovative healthcare system [ 9 , 10 , 11 , 12 , 13 , 14 ]. Using metaverse capabilities, experts prepare 3D images of the organ in question, enabling them to interpret the images more quickly and accurately [ 15 ]. The use of multimodal images, which results from integrating several images, is one of the new and accurate methods in improving the evaluation, diagnosis, and treatment of diseases in the imaging field [ 16 ]. Studies show that a medical imaging method alone cannot provide complete and accurate information. In modern research, the multimodal medical image fusion approach is one of the significant fields in medical imaging. Combining medical images from one or more imaging methods, increasing image quality, and random acquisition, improves the clinical utility of medical images [ 16 , 17 , 18 , 19 ]. Metaverse is considered a facilitating technology in the creation of integrated imaging methods. Therefore, it can be widely used in radiology and medical imaging [ 16 ]. In image transmission, sometimes, due to the high volume of images, their transmission is disrupted, so using a platform faster than the Internet to transmit more information and better‐quality images, such as Metaverse, is on the agenda [ 6 ]. Due to the importance of the subject, studies have been conducted in this field in recent years. Garavand and his colleagues, in their research to investigate and identify the application areas of emerging Metaverse technology, state that the main services for using Metaverse include educational services, intervention services, and communication services. Also, medical imaging is the most applied aspect of the Metaverse in healthcare [ 6 ]. Wang et al. also presented Metaverse use cases, including virtual comparative scanning, raw data sharing, augmented surveillance science, and Metaverse medical intervention [ 20 ]. They state that the Metaverse will improve medical practice based on artificial intelligence, especially diagnosis and treatment guided by medical imaging. Bhugaonkar et al., in their study, show that the Metaverse has a huge potential in healthcare for combining artificial intelligence technologies, virtual reality, augmented reality, the web, the Internet of medical devices, and quantum computing [ 21 ]. However, a specific study or review has not been done concerning the use of Metaverse in medical imaging. Therefore, the key objectives of this systematic review are to evaluate the current applications of the Metaverse in radiology and medical imaging, to categorize the potential use cases of Metaverse technologies in imaging procedures, to assess the benefits and challenges of Metaverse integration in radiology practice, and to provide evidence‐based insights that can guide future adoption and decision‐making in this field.

The authors declare no conflicts of interest.

Supporting file 1.