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Acceptance and use of extended reality in surgical training: an umbrella review

Systematic Reviews volume 13, Article number: 299 (2024) Cite this article

Abstract

Background

Extended reality (XR) technologies which include virtual, augmented, and mixed reality have significant potential in surgical training, because they can help to eliminate the limitations of traditional methods. This umbrella review aimed to investigate factors that influence the acceptance and use of XR in surgical training using the unified theory of acceptance and use of technology (UTAUT) model.

Methods

An umbrella review was conducted in 2024 by searching various databases until the end of 2023. Studies were selected based on the predefined eligibility criteria and analyzed using the components of the UTAUT model. The quality and risk of bias of the selected studies were assessed, and the findings were reported descriptively.

Results

A total of 44 articles were included in this study. In most studies, XR technologies were used for surgical training of orthopedics, neurology, and laparoscopy. Based on the UTAUT model, the findings indicated that XR technologies improved surgical skills and procedural accuracy while simultaneously reducing risks and operating room time (performance expectancy). In terms of effort expectancy, user-friendly systems were accessible for the trainees with various levels of expertise. From a social influence standpoint, XR technologies enhanced learning by providing positive feedback from experienced surgeons during surgical training. In addition, facilitating conditions emphasized the importance of resource availability and addressing technical and financial limitations to maximize the effectiveness of XR technologies in surgical training.

Conclusions

XR technologies significantly improve surgical training by increasing skills and procedural accuracy. Although adoption is facilitated by designing user-friendly interfaces and positive social influences, financial and resource challenges must be overcome, too. The successful integration of XR into surgical training necessitates careful curriculum design and resource allocation. Future research should focus on overcoming these barriers, so that XR can fully realize its potential in surgical training.

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Introduction

Surgical training requires lifelong learning of cognitive and practical skills, such as comprehensive pattern identification and proficiency with distraction-minimization strategies during surgical operations [1]. In fact, both surgical skills and patient safety depend critically on these abilities [1]. Traditional surgical training techniques have many limitations and are practiced on cadavers or animal models. Beyond the risks of spreading infectious diseases, these approaches are expensive, ethically dubious, and not widely available [1, 2]. Similarly, while artificial models serve as valuable introductory tools, they may not be particularly effective at simulating realistic physiological responses [3]. Moreover, trainees experience more difficulties when using traditional surgical training methods due to inaccessibility of professional surgeons and the intrinsic challenges with surgical procedures [1]. Therefore, it seems that simulation environments are effective ways to overcome these challenges [3]. In fact, simulation systems provide a dynamic learning environment which offers a new opportunity to engage trainees with virtual patients [4]. With these technologies, users are immersed in virtual environments that either mimic or deviate from actual surgical scenarios [5]. However, it is very difficult to convert complex three-dimensional (3D) data, like manipulating medical equipment and anatomy, onto a two-dimensional (2D) display [4, 5].

Extended reality (XR) consists of virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies and provides a novel solution to the limitations of traditional surgical training [6, 7]. VR tools immerse users in fully digital environments, often simulating real-world settings, allowing them to interact with a virtual environment in a controlled space without real-world risks [8]. AR overlays digital content onto the real-world environment, enabling users to interact with virtual elements while maintaining a clear view of their physical surroundings [9]. MR blends both VR and AR, where digital and physical elements coexist and interact in real time, allowing users to manipulate virtual objects as if they were part of the real world [10].

The XR technologies enhance visualization and provide real-time error identification, fostering a deeper understanding of 3D anatomy, complex procedures, and surgical techniques [11]. Moreover, XR systems can be tailored to meet the unique needs of individual trainees, simulating specific anatomical scenarios for customized learning experiences [6, 11]. By allowing remote training and information sharing, XR immediately not only improves education but also signals a paradigm shift in surgical training [11, 12]. In addition, XR facilitates knowledge transfer between learners and instructors at different times and locations, which is particularly beneficial in areas with limited access to relevant expertise [13]. Despite the great potentials of XR, the adoption rate of this technology in surgical training has been limited. A major gap in the literature is related to the lack of an extensive understanding of the factors that may influence the widespread adoption and use of XR in surgical training [14].

Recently, several reviews have been published on the use of various XR technologies in different areas of surgical training [6, 12,13,14]. Although their overall results highlight the role of XR in surgical training, there are still factors influencing the widespread acceptance and use of this technology. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is a widely accepted framework for understanding the key factors that influence the adoption of technologies. This model has four domains including performance expectancy, effort expectancy, social influence, and facilitating conditions which offer valuable insights into the drivers and barriers faced by users [15]. It seems that using this model along with synthesizing and analyzing findings from multiple systematic reviews and/or meta-analyses can help to highlight the impact of various factors on the acceptance and use of XR technologies in surgical training and can provide insights for future research. Therefore, this study aimed to conduct an umbrella review to investigate factors influencing the acceptance and use of XR in surgical training. The results of this study can be used in improving future research for the development, implementation, and effective use of XR-related technologies in surgical training.

Methods

Study design

This umbrella review adheres to the guidelines outlined in the Preferred Reporting Items for Overviews of Reviews (PRIOR) [16]. The National Ethics Committee of Biomedical Research (IR.IUMS.REC.1402.1208) reviewed and approved this study.

Information sources and search strategy

A comprehensive literature search was conducted across multiple databases including the Cochrane Database of Systematic Reviews, PubMed, Scopus, Web of Science, IEEE Xplore, Ovid, ProQuest, and Google Scholar. The search was limited to publications in English, focusing on the systematic reviews and meta-analyses up to December 31, 2023. The search strategy included key concepts and their synonyms like “systematic review,” “extended reality,” “training,” and “surgery,” combined with AND/OR logical operators (Supplementary Table S1). Additionally, manual search of citations and reference lists of the included studies were performed.

Eligibility criteria

The inclusion and exclusion criteria are presented in Table 1.

Table 1 Inclusion and exclusion criteria
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Selection process

Following comprehensive database search and removing duplicates using EndNote 8, two authors (El. T. and Es. T.) independently reviewed the titles and abstracts of the retrieved studies to assess their relevance. Disagreements that arose during this stage were limited and primarily related to whether certain studies aligned with the inclusion criteria, such as surgical training and using XR technologies. In addition, a third author (H. A. or M. F.) was consulted to make the final decision, if it was necessary. After identifying the relevant studies, the full texts were independently reviewed by both authors (Es. T. and El. T.) to confirm their eligibility for inclusion based on the predefined criteria. Disagreements during this phase usually stemmed from questions about the scope of the interventions. These disagreements were resolved through discussions or, if necessary, by consulting the third author (H. A.).

Data collection process, data items, and synthesis method

Data extraction was conducted systematically by one of the authors (El. T.) using a predefined data extraction form, and the accuracy and completeness of the results were verified by second author (Es. T.). Extracted information included author name, publication year, country, research objective, a summary of the interventions, main results, participants, comparators, outcomes, searched databases, study design, qualitative assessment results, and risk-of-bias assessment.

In this study, conducting a meta-analysis was not possible. Significant heterogeneity was observed across the included studies. The variation occurred in outcome measures also differed substantially, with some studies focusing on technical skill acquisition, task completion times, and error rates, while others emphasized cognitive outcomes or knowledge retention. Different types of participants added to this heterogeneity, as a range of surgical trainees, from medical students to experienced surgeons participated in the studies, and each of them might require different surgical skill. This heterogeneity precluded a quantitative meta-analysis, prompting us to use a qualitative framework synthesis. We used the UTAUT [15] as a guiding framework to categorize and analyze factors influencing the acceptance and use of XR technologies in surgical training. The UTAUT framework comprises performance expectancy, effort expectancy, social influence, and facilitating conditions, providing a structured and theory-driven approach to analyzing the data.

Both authors (Es. T. and El. T.) independently coded the findings using qualitative software, MAXQDA (version 18.2.5), and any discrepancies in the coding were resolved through discussion. A third author (H. A.) was consulted for further resolution when necessary.

Critical appraisal assessments of the included reviews

Two authors (Es. T. and El. T.) independently assessed the methodological quality and risk of bias of each systematic review and meta-analysis using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR 2) and the Risk of Bias in Systematic Reviews (ROBIS) tool.

AMSTAR 2 was used to assess specific methodological domains within the included systematic reviews, such as protocol registration, study design, and data analysis methods [17]. AMSTAR 2 has not been designed to provide an overall score for the quality of systematic reviews; rather, it focuses on critical methodological aspects, helping to categorize studies into quality tiers (i.e., “high,” “moderate,” “low,” or “critically low”), based on their adherence to best practices in systematic review methodology. This distinction is crucial to avoid confusion, as AMSTAR 2 does not offer a single composite score of overall quality but assesses distinct domains that inform the rigor of each study’s methods.

ROBIS tool was used to ensure a comprehensive evaluation of bias across various stages of the review process [18]. This tool examines biases in study selection, data collection, and synthesis, providing a more holistic view of risk that complements AMSTAR 2 domain-specific approach. ROBIS involves three phases: (1) relevance of the review to the research question, (2) assessment of biases across four domains (study eligibility criteria, identification and selection of studies, data collection and appraisal, and synthesis and findings), and (3) an overall risk-of-bias judgment (high, low, or unclear). Together, these tools offer a comprehensive evaluation, ensuring that both methodological quality and bias were critically appraised.

To address overlapping primary studies included in multiple reviews, a citation matrix, as suggested by Pieper et al. [19], was created to represent primary study overlap across systematic reviews. The degree of overlap was narratively described, and we calculated the “corrected covered area” (CCA) to quantify the extent of overlap.

Results

Systematic review study selection

After considering inclusion and exclusion criteria, 44 articles [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63] were included in this study. Figure 1 shows the screening process for the included articles. In addition, Table 2 and Supplementary Table S2 provide a summary of articles which were included in the study.

Table 2 The main characteristics of the included studies
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Fig. 1
figure 1

Article selection process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [64]

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Characteristics of the systematic reviews

As Table 2 shows, in total, 44 systematic reviews were included, of which 37 studies were conducted as systematic reviews [20,21,22,23,24, 26,27,28,29,30,31,32,33,34,35, 37,38,39,40, 42, 44,45,46,47,48,49, 51, 53,54,55,56,57,58,59,60,61,62], 4 studies as meta-analyses [36, 41, 50, 63], and 3 studies as systematic review and meta-analyses [25, 43, 52]. Most of these studies were conducted in the UK (n = 15, 34%) [21, 27, 33, 35, 36, 38, 45,46,47, 49, 50, 54, 56, 59, 62], the USA (n = 9, 20%) [20, 22, 30, 37, 39, 51,52,53, 63], Italy (n = 4, 9%) [24, 26, 34, 48], and Australia (n = 3, 6.8%) [42, 44, 55]. China [25, 32], Denmark [58, 60], and Canada [28, 43] each contributed to publish two studies. Other studies were conducted in Finland [61], the Netherlands [31], Belgium [23], Germany [41], Switzerland [29], Brazil [40], and France [57] each conducting one study. Furthermore, most of the included studies (n = 13, 29.5%) were published in 2023 [20,21,22,23,24,25,26,27,28,29,30,31,32].

In most studies, XR-related technologies were used to improve training in various surgical areas including orthopedics (n = 15) [20, 22, 25, 27, 28, 30, 32, 34, 37, 44, 45, 47, 51, 52, 57], neurology (n = 9) [21, 22, 27, 29, 30, 33, 44, 47, 52], laparoscopy (n = 9) [32, 36, 40, 50, 56, 60,61,62,63], genitourinary (n = 8) [21, 39, 41, 44, 46,47,48, 63], and otorhinolaryngology (n = 4) [26, 38, 55, 59]. Furthermore, compared to other XR-related technologies, VR was the most reported intervention in surgical training (n = 26) [24, 29,30,31, 35,36,37, 40,41,42,43, 45, 48,49,50,51, 54,55,56,57,58,59,60,61,62,63]. In these studies, the main aim was to improve the trainee’s operative performance (n = 9), reduce the time to complete the tasks (n = 8), improve patient clinical outcomes (n = 5), and surgical skill transfer (n = 5).

In seven studies, AR surgical training tools [21, 22, 33, 34, 44, 46, 47] were used as interventions which aimed to improve patient clinical outcomes (n = 3), training effectiveness (n = 3), and surgical accuracy (n = 2). Other review studies (n = 11) [20, 23, 25,26,27,28, 32, 38, 39, 52, 53] reported applications of AR, VR, or MR tools in surgical training.

Primary study overlap

A citation matrix mapped the overlapping primary studies, identifying that 19 reviews shared at least 1 primary study. The extent of overlap ranged from minimal (overlap in only 1–2 studies) to significant (overlap in more than 10 studies). In this study, the CCA was 3.75% which indicates a low level of primary study overlap across the included reviews. The CCA formula is as follows:

$$CCA=\frac{N-r}{rc-r}=\frac{217-83}{83\times44-83}=0.0375$$

N = total number of occurrences of primary studies in all systematic reviews

r = number of unique primary studies across all systematic reviews

c = number of systematic reviews included in the analysis

The overlap predominantly occurred in studies examining XR-based surgical training interventions, particularly in laparoscopic surgery and orthopedics. To minimize bias, we discussed the results of overlapping studies only once in our analysis. The overall findings presented in the UTAUT model categories were adjusted to account for these overlaps.

Quality and risk of bias in the studies

The quality of the studies was assessed using the AMSTAR 2 [17]. According to the defined criteria, most of the included studies (n = 39) were rated as “critically low” quality [20,21,22,23, 25,26,27,28,29,30, 32, 34,35,36,37,38,39,40,41,42,43,44, 46,47,48,49,50,51,52,53,54,55, 57,58,59,60,61,62,63]. Other studies were rated as “low” (n = 3) [31, 33, 45], “moderate” (n = 1) [56], or “high” (n = 1) [24] quality. The main reasons for low-quality ratings were linked to the specific AMSTAR 2 items. First, many reviews did not include a registered protocol (Item 2), which introduced potential bias due to unreported changes in the methodology during the review process. In addition, several studies failed to report the sources of funding for the included studies (Item 9) which compromised the transparency of the research. Another key issue was related to the inadequate risk-of-bias assessment in the individual studies (Item 10), which undermined the reliability of the conclusions. Finally, many studies did not provide adequate explanations for heterogeneity across their findings (Item 13), affecting the robustness of the results. Additional details about quality assessment were provided in Fig. 2 and Supplementary Table S3.

Fig. 2
figure 2

Quality assessment of the included studies (AMSTAR 2)

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The included studies were also assessed for the risk of bias using the ROBIS tool [18], which examines biases across four domains: study eligibility criteria, identification and selection of studies, data collection and appraisal, and synthesis and findings. The results showed that 45.4% were classified as having a “low risk” of bias (n = 20), 20.4% had an “unclear risk,” and 34% had a “high risk” of bias. The primary reasons for a high risk of bias were mainly related to Domain 3 (items 4 and 5), which described concerns over inadequate data collection and appraisal methodologies. In Domain 4 (items 3, 5, and 6), problems emerged concerning the synthesis and presentation of the findings, including a failure to account for heterogeneity and insufficient justification for the conclusions drawn. Moreover, regarding the judgment of overall risk of bias (items 2 and 3), inconsistencies in the application of bias assessment criteria across the included studies frequently impacted the overall review quality. More details about risk-of-bias assessment were provided in Fig. 3 and Supplementary Table S4.

Fig. 3
figure 3

Risk-of-bias assessment (ROBIS tool)

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Factors influencing acceptance and use of XR technology in surgical training

Based on the UTAUT model, factors influencing the acceptance and use of XR technology in surgical training were divided into four domains: performance expectancy, effort expectancy, social influence, and facilitating conditions. The following sections outline the results of the reviewed studies for each of these categories.

Performance expectancy

The findings indicated a positive correlation between VR training and the improvement of surgical skills in various fields including laparoscopy [32, 36, 50, 60,61,62], orthopedics [24,25,26, 37, 45, 51], otolaryngology [32, 55, 57, 59], neurology [26, 27, 54], plastic surgery [53], and urology [27, 39, 46, 48]. VR technologies provide trainees with a controlled and safe environment for repeated practice of complex surgeries [38, 54] and improve both mental and motor skills in experienced surgeons [59]. VR simulations also support preoperative planning [38] and reduce the risks associated with live surgeries by allowing trainees to practice complex procedures, such as aneurysm clipping, hernia repair, and cataract surgery [29, 35, 42, 53, 58]. The realistic 3D images provided by VR help improve technical skills and decision-making [26, 29, 35, 36, 42, 48] and reduce operating room time and complications [20]. Moreover, VR training enhances nontechnical skills like teamwork and communication [27], improves surgical anatomy understanding [42], and effectively simulates the operating room environment by offering auditory, visual, and sensory feedback [35, 48, 54].

Additionally, VR reduces surgical task completion time [43, 50, 51, 62, 63], increases movement efficiency [50, 51, 63], improves handling of surgical instruments [63], and minimizes intraoperative errors [26, 63]. Compared to traditional training methods, VR enables better supervision, decreases training costs [26, 38], reduces fatigue, enhances learning experiences [41, 50], provides targeted training programs [24, 32, 37, 40], and fosters better comprehension and confidence [21, 46, 53]. Overall, studies affirm VR simulators improve surgical skills for all experience levels [22, 44, 46, 48, 54, 56], as evidenced by metrics such as time, path length, successful procedure completion, and trainee confidence [43, 48, 55, 56].

AR technologies also make significant contributions to surgical training by improving comprehension, precision, and confidence, particularly in complex surgeries [22, 23, 33, 47]. AR-based systems offer real-time visualizations [22, 33], personalized navigation, and feedback [21, 23, 28, 33, 44], allowing surgeons to enhance accuracy [20, 22, 33, 34] and minimize errors [28] in procedures like implant placement [39] and neurosurgery [22, 33].

Furthermore, XR technologies provide an immersive training environment, improving surgical skills and precision while potentially reducing errors across various surgical disciplines [28, 29, 32, 38, 52, 53]. Studies highlight the effectiveness of XR in reducing operation time and workload in thoracic surgeries [38], enhancing accuracy in procedures like femoral stem screw placement and total hip arthroplasty [25, 28], and reducing fluoroscopy time in minimally invasive percutaneous surgeries [28].

Effort expectancy

According to the results, both VR and AR interfaces are commonly regarded as intuitive, especially for users with different degrees of technical expertise [63]. This is especially accurate for VR, as the interfaces are specifically designed to be user-friendly and simple to understand and navigate [63]. Surgeons view AR as a more efficient and user-friendly teaching tool in comparison to traditional approaches [31, 34, 43]. The incorporation of preexisting technologies such as C-arm imaging improves the ease of use and provides real-time visualization during AR training [34].

VR simulations are highly effective in creating realistic environments that replicate real-world surgical situations, allowing trainees to improve their skills in a secure setting with prompt feedback [63]. AR, particularly when using head-mounted displays (HMDs), also offers realistic task representations beneficial for pre-surgical training [22, 23, 34, 46]. However, AR may lack realism, 3D perception, and immersive performance compared to VR and standard box trainers [21]. Positive feedback from experienced surgeons can enhance the acceptance and utilization of XR technologies among trainees, highlighting their value in improving surgical skills [20]. Despite these benefits, more research is needed to fully realize the potential of AR and VR in realistic training, especially in delicate procedures like plastic surgery, due to current limitations in surgical simulation [53].

Social influence

Studies showed that VR simulations incorporating virtual instructors or peers, particularly experts, can improve skill acquisition for novice surgeons, as evidenced in research on gynecological procedures [48]. However, widespread VR adoption is hindered by financial constraints [41]. The initial investment required for VR equipment, which includes not only the HMDs but also any additional hardware or software required for an effective VR experience, represents a significant financial barrier for both individual and institutional users, potentially limiting accessibility [41]. This concern is reflected in AR simulation research, which has identified high HMD costs as a major barrier to adoption [46]. Further research into more cost-effective options, such as AR simulations that use cadaver heads, is critical to addressing these restrictions and ensuring VR and AR technologies reach a wider audience within surgical training programs [26].

Facilitating conditions

The results showed that VR is effective in teaching essential laparoscopic skills and allows for deliberate practice in a controlled environment, fostering focused skill development [48, 50]. Furthermore, VR training is accessible and beneficial in resource-constrained areas [27]. Also, immersive VR training is potentially improving performance, especially in developing cognitive abilities under stress [43]. However, successful integration requires careful curriculum design, particularly for less complex procedures like laparoscopic cholecystectomies [61]. In addition, customizing session duration, frequency, and learner support mechanisms can enhance the effectiveness of VR surgical training tools [31, 59]. Factors such as task complexity and prior VR experiences also influence user perception and training effectiveness [49].

However, concerns about the transferability of skills from VR to the operating room remain due to difficulties in simulating real-world issues [32] and the entire surgical workflow [29, 45]. It is particularly beneficial in specialized teaching areas like pediatric neurosurgery, where case availability and technical limitations pose challenges [27]. Nevertheless, VR’s limitations include reproducing the entire surgical workflow, tactile feedback, and pressure sensations [50], which reduce its effectiveness in complex procedures like intracranial aneurysm repair [29]. Also, concerns about the difference between VR’s visual fidelity and the operating room environment require further research [26].

In AR surgical training tools, studies reported that user interaction with 2D/3D anatomical overlays is particularly challenging [23]. Technical constraints like as battery life and overheating in HMDs might cause user discomfort during training sessions [46]. Furthermore, issues in giving realistic haptic feedback as compared to traditional training techniques based on cadaveric models must be overcome before AR widely adoption [47].

Synthesis of the results

The adoption of XR technologies in surgical training, based on the UTAUT model, is influenced by several key factors across four domains: performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy refers to the value of VR and AR technologies in enhancing both technical and nontechnical skills, particularly in high-risk surgeries. These technologies provide immersive, realistic environments that improve decision-making, accuracy, and confidence in surgical trainees. From an effort expectancy perspective, XR technologies are generally considered user-friendly, although VR tools are often viewed as more immersive and realistic compared to other XR technologies. Social influence emphasizes the role of expert endorsement in promoting the adoption of XR among trainees. Facilitating conditions highlight the importance of well-designed curricula and access to resources, although challenges such as skill transfer to real-world settings and technical limitations, like haptic feedback and system realism, still hinder the full integration of XR technologies.

Discussion

Principle findings

This umbrella review explored the potential key factors influencing the acceptance and use of XR in surgical training based on the UTAUT model which included performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy reveals that VR and AR significantly enhanced surgical skills across disciplines by offering immersive, realistic simulations that improve psychomotor skills, decision-making, and procedural accuracy while reducing risks and operating room time. Effort expectancy highlighted XR interfaces’ user-friendly and intuitive nature, especially VR, making them accessible to trainees with various technical expertises. Social influence underscores the experienced surgeons play a key role in promoting XR adoption by providing positive feedback and serving as advocates for the technology, which helps build trust and encourages use among trainees. Facilitating conditions emphasize the necessity for careful curriculum design, resource availability, and addressing technical limitations to maximize the effectiveness of XR technologies in surgical training. Moreover, financial constraints, particularly the high cost of hardware and software, remain a significant barrier.

The results showed a positive correlation between VR training and enhanced surgical skills across various disciplines. VR provides a controlled, safe environment for surgeons to practice complex procedures, improving proficiency and reducing error rates [65, 66]. Studies emphasize VR’s role in reducing the learning curve for endoscopic sinus surgery through realistic anatomical visualizations and haptic feedback, which enhance procedural accuracy and confidence [67, 68]. Recent advancements in VR technology, such as artificial intelligence (AI) integration, have further improved the realism and interactivity of simulations, making virtual environments closely resemble real-world surgical environments [69]. Different immersive technologies serve distinct roles in surgical training: fully immersive VR offers comprehensive sensory engagement for intricate procedures, semi-immersive VR integrates virtual and real-world elements for collaborative training, and non-immersive VR is practical and cost-effective for initial training stages [70, 71].

AR significantly enhances trainee comprehension, skills, and confidence. Similarly, studies showed that AR-based navigation systems improve the accuracy of surgical interventions and reduce operative times in neurosurgery [72]. A study also found that AR improves the precision of orthopedic surgical procedures [73]. In AR, marker-based systems provide precise visual guidance beneficial in orthopedic surgeries [74], while marker-less AR offers greater flexibility in dynamic settings such as maxillofacial surgery [75].

Effort expectancy, including the ease of use and learning associated with XR technologies, is essential for their adoption. As the results showed, both VR and AR interfaces are generally perceived as intuitive, enhancing the learning curve for surgical trainees. This has been highlighted by McKnight et al., too [76]. Fully immersive VR is ideal for complex surgical training due to its comprehensive sensory engagement and makes it easier to use in surgical training [77]. In addition, studies showed that marker-based AR systems provide precise overlays in surgical training but require physical markers that make them complex to use, while marker-less AR offers greater flexibility using computer vision techniques [71]. Advanced haptic feedback in AR systems shows promise in enhancing realism and training effectiveness [75].

Social influence significantly affects the adoption of XR technology in surgical training. Similarly, studies showed that incorporating virtual instructors or peers, particularly experts, in VR simulations significantly improves skill acquisition [76, 78]. According to the current findings, the area of adoption received less attention, and the impact of XR proficiency on enhancing a surgeon’s image or status within an organization remains unclear. It is essential for organizational culture and reference groups to value surgeons’ ability to work with these technologies [79]. Surgeons often rely on their peers and colleagues to validate the relevance and benefits of using XR technologies. This social dynamic is particularly influential in environments where early adopters advocate for XR use and respected colleagues demonstrate its value in practice [80]. Studies have shown that when surgeons observe peers successfully using XR for complex surgical simulations, they are more likely to integrate it into their own practice [6, 11]. In such settings, peer-led training sessions or informal mentoring plays a significant role in overcoming initial skepticism. For instance, orthotics residents have cited the influence of peers demonstrating improved surgical precision and reduced operative time using VR as a turning point for their adoption of the technology [81].

According to the literature, working with XR is often viewed as ineffective or time-consuming [82]. However, other research highlights the crucial effectiveness of XR in surgical environments [6, 13]. Studies demonstrated that institutions prioritizing traditional training methods saw lower uptake of XR, particularly when surgical trainers felt pressured to maintain efficiency without significant investment in training [83, 84]. An example can be drawn from the study by Nanashima et al., which found that in a traditional hospital setting, only 18% of digestive surgery trainers reported adopting VR, compared to 64% adoption in a more technologically progressive hospital where VR was systematically integrated into daily surgical training programs [85]. Therefore, new strategies should be organized to improve understanding among key influencers in the surgical field regarding the benefits of XR. Social VR platforms enable collaborative learning in shared virtual environments, overcoming geographical barriers and facilitating global interaction [86]. Avatars and virtual representations enhance presence and engagement, making training experiences more immersive and realistic [87].

Facilitating conditions, including resource availability and support for XR technologies, are crucial for adoption. Similar to current study findings, studies reported VR is effective in increasing the performance of surgical training in resource-constrained areas [66, 83]. To design XR technologies compatible with the values and needs of surgeons in the current environment, these considerations must be addressed [70, 88]. In addition, special instructions and facilitating conditions need to be provided to improve trainees’ attitudes towards the adoption of XR [11]. Integrating MR technologies enhances training by blending real and virtual elements, improving spatial understanding and surgical accuracy [89]. Advanced technologies like AI and cloud computing facilitate XR adoption by providing intelligent feedback and adaptive learning pathways, reducing cost barriers through sophisticated applications without expensive local hardware [69, 71, 90]. Moreover, incorporating advanced hardware tools, such as haptic devices and motion capture systems, enhances the realism of XR training and skill acquisition [71].

While XR offers a number of benefits, including immersive simulations that enhance psychomotor skills, procedural accuracy, and decision-making, the initial cost of implementing these tools may limit their widespread use. A review study demonstrated the potential of affordable XR tools, such as using mobile-based AR or low-cost VR headsets that offer similar training advantages at a fraction of the cost of high-end systems [91]. For example, mobile-based VR applications like smartphone-compatible devices can provide useful anatomical overlays without using expensive headsets or operating room modifications [92]. Moreover, adopting a phased implementation strategy, where lower-cost XR solutions are gradually integrated into training programs, can help institutions to manage the financial impact [93]. This approach allows trainees to benefit from fundamental XR capabilities while reducing the upfront investment. A successful example is the integration of basic VR simulators for the initial stages of surgical training, with more advanced, expensive systems reserved for senior surgical trainees focusing on the complex procedures [94].

Implication for practice and future studies

According to the findings, it seems that social influence and facilitating conditions received less attention in designing XR technologies, despite the well-accepted and understood importance of effort and performance expectancy among XR designers. To leverage XR technologies effectively in surgical training, developers should invest in user-friendly and intuitive VR and AR interfaces to make adoption smoother for trainees of various medical expertises. Financial investment and cost-effective solutions are needed to overcome the initial cost barriers of XR hardware. Implementing new curriculums that incorporate VR and AR training modules can significantly enhance the skill acquisition process, especially for complex surgical procedures. In addition, customization of training sessions to fit individual learner’s requirements can optimize the learning experience.

Future research should focus on improving the realism of VR and AR simulations, particularly in replicating tactile feedback and pressure sensations, which are critical for certain surgical procedures. Developing more advanced haptic feedback systems and addressing technical limitations like battery life and overheating in AR headsets will enhance user experience. Moreover, future research should prioritize longitudinal studies and randomized controlled trials to assess the long-term effectiveness and skill transferability of XR on surgical performance, as the current body of evidence is largely based on short-term studies. Such research is critical to understanding how XR technologies translate into real-world performance and whether the initial gains in skill acquisition are sustained over time. These studies should aim to measure the retention of surgical skills over extended periods and determine whether XR-based training leads to lasting improvements in clinical outcomes, such as reduced operating room times and fewer complications in actual surgeries.

Study limitations

This study had some limitations. Firstly, non-English articles were excluded mainly due to the limited resources for translation and analysis. Secondly, original research studies included in the selected reviews were not analyzed directly. This may have affected capturing the full range of evidence on the factors affecting XR adoption in surgical training. In future, original studies can be examined to explore factors influencing the acceptance and use of XR technologies in surgical training.

Conclusion

This study highlighted factors influencing the acceptance and use of XR interventions in the surgical settings. XR has demonstrated effectiveness in various surgical disciplines, reducing risks and time in the operating room while boosting trainee’s confidence and proficiency. The intuitive nature of these technologies promotes their acceptance among trainees. However, financial constraints and technical limitations hinder widespread adoption. Further research is essential to address these challenges and validate their long-term impact on surgical training and performance.

Data availability

The data that support the findings of this study are available from the corresponding author (H. A.), upon reasonable request.

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Acknowledgements

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Funding

This research was funded and supported by the Iran University of Medical Sciences, Tehran, Iran (1402–4-99–28214).

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Authors and Affiliations

  1. Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran

    Esmaeel Toni

  2. Health Information Technology, Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran

    Elham Toni

  3. Medical Informatics, Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran

    Mahsa Fereidooni

  4. Medical Informatics, Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran

    Haleh Ayatollahi

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Contributions

Conceptualization, EsT, ElT, MF, and HA; methodology, EsT, ElT, MF, and HA; validation, HA and EsT; formal analysis, ElT and EsT; investigation, ElT and EsT; writing—original draft, EsT; writing—review and editing, EsT and HA; and supervision, HA. All authors have read and agreed to publish the manuscript.

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Correspondence to Haleh Ayatollahi.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Iran University of Medical Sciences (IR.IUMS.REC.1402.1208). Not applicable.

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All authors agreed with publication.

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The authors declare that they have no competing interests.

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Supplementary Information

13643_2024_2723_MOESM1_ESM.docx

Additional file 1: Table S1. Search strategies. Table S2. Additional characteristics of included studies. Table S3. Quality assessment of included studies (Based on AMSTAR 2 Checklist). Table S4. Risk of bias assessment of included studies.

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Toni, E., Toni, E., Fereidooni, M. et al. Acceptance and use of extended reality in surgical training: an umbrella review. Syst Rev 13, 299 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-024-02723-w

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Keywords

Systematic Reviews

ISSN: 2046-4053