The rapidly expanding field of robot-assisted surgery necessitates a parallel evolution in surgical education. A surgeon introduced to the telemanipulator control system can count on technological support that increases precision and supports decisions made during surgery. Generally, the surgeon (operator) is an integral part of the robot, so the synergy of this cooperation may bring the expected progress in access to high-quality services for many patients.
Methods
This review explores the current state of robotic surgery education, analyzing its limitations and established applications. Additionally, it delves into promising future directions, including the potential of artificial intelligence and advancements in training methods.
Results
This review identifies key challenges and highlights innovative strategies such as virtual reality simulation and cadaveric training. Furthermore, it emphasizes the importance of developing standardized national curricula to ensure consistent training quality.
Conclusion
This review emphasizes the need for a robust educational framework to equip surgeons with the necessary skills for safe and effective robotic surgery integration. The use of high-tech tools also requires the use of innovative educational methods. By embracing innovative technologies and prioritizing a standardized curriculum, we can ensure that the future of surgical training empowers surgeons and ultimately improves patient outcomes.
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Key points
This review unveils the potential of virtual reality (VR) simulation in robot-assisted surgical (RAS) training.
VR simulation offers personalized feedback and guidance for trainees, thereby pushing learning beyond current capabilities.
By providing a deeper analysis of challenges like the learning curve and haptic feedback limitations, this review offers a roadmap for optimizing future RAS education.
Introduction
Robotic devices have become increasingly prevalent in operating rooms. Their integration into surgical practices began in the 1980s with the introduction of the PUMA 560 robot (Unimation, Danbury, Connecticut) for brain biopsy, initially designed to mitigate surgeon hand tremor and involuntary movements [1, 2]. This pioneering effort paved the way for more sophisticated robotic systems capable of performing complex surgeries.
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A key advantage of robot-assisted surgical (RAS) procedures is their high degree of accuracy, which is particularly valuable in intricate surgeries requiring millimeter precision [3]. This enhanced precision enables surgeons to perform a wide range of challenging procedures in hard-to-reach areas of the body, including cardiac (atrial septal defects, cardiac tumors, mitral valve), gastrointestinal (colectomy, proctectomy, gastrectomy, pancreatectomy), general (appendectomy, cholecystectomy), and thoracic surgery (lung resection, thymectomy), as well as many other types [4‐8].
The constant evolution of robotic surgical technology has led to the introduction of highly dexterous robotic arms, miniaturized surgical instruments, and advanced 3D imaging and visualization systems. These advancements significantly improve the precision with which surgeons can navigate surgical instruments within the operative field. Additionally, robotic surgical systems introduce haptic feedback, allowing surgeons to assess tissue consistency and adjust the force applied to tissues, thereby minimizing complications associated with excessive force. Telesurgery, a technology currently under development, utilizes robotic systems to perform surgical procedures remotely. This holds promise for delivering surgical care to underserved areas with limited access to surgical specialists [9, 10].
The advancements in robot-assisted surgical procedures necessitate a parallel focus on surgeon education. Surgeons must develop proficiency in manipulating surgical instruments within the robotic system. Additionally, the ability to effectively utilize 3D visualization systems significantly influences the outcome of robotic surgeries. Implementing structured and standardized training programs can equip surgeons with the skills necessary to minimize potential complications arising from inadequate training. Effective surgical education is crucial for developing the expertise of both surgeons and robotic system operators [11‐13].
This comprehensive review aims to synthesize current educational methods employed to equip surgeons with the skills necessary to perform robot-assisted surgeries.
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Literature analysis
To identify relevant articles for this review, a comprehensive search was conducted in two popular medical databases: PubMed and Scopus. Medical Subject Headings (MeSH) terms were utilized to retrieve articles related to robotic surgical education. These MeSH terms included “robot surgery,” “robot-assisted surgery,” “education,” “training,” and “curriculum.” This search strategy yielded a total of 4944 articles in PubMed and 4227 articles in the Scopus database.
Employing Scopus’ analytical tools, the authors further investigated trends in published articles on robotic surgical education. The analysis revealed a significant increase in the number of publications, with over 611 articles published in 2023 alone (Fig. 1). This represents a twofold increase compared to 2019 (311 articles) and a sixfold increase compared to 2013 (110 articles).
Fig. 1
Documents by year related to the subject of robotic surgery education. Prepared with the Scopus database
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The analysis of publication channels indicated that the most frequent venues for publishing research on robotic surgical education were the Journal of Robotic Surgery (230 articles), Surgical Endoscopy (182 articles), and the Journal of Endourology (84 articles). John Hopkins University (85 articles), Imperial College London (81 articles), and Onze Lieve Vrouw Hospital (67 articles) emerged as the leading institutions with the highest numbers of published articles in this field.
Unsurprisingly, the United States emerged as the global leader in publishing education-related articles on robotic surgery, with over 1727 publications. This represents a significant lead over the UK (477 articles) and Italy (403 articles), which ranked second and third, respectively.
The analysis of publication types revealed that over 2518 articles were classified as original research papers, followed by 640 review articles and 474 editorials. With regards to funding sources, the National Institutes of Health (87 publications), Intuitive Surgical (83 publications), and the National Natural Science Foundation of China (75 publications) were identified as the most common sponsors of research in this domain.
Main obstacles
The rapidly increasing field of robot-assisted surgery presents distinct challenges in the domain of surgeon education [14‐19]. The learning curve connected with RAS technology shows both hindrances and opportunities—this is mainly caused by (and significantly different to) the previous surgical experience of the operator. For surgeons who are already familiar with laparoscopic surgery, transitioning to RAS involves adapting to the technical aspects of the robotic workstations. These surgeons are sufficiently experienced in traditional minimally invasive surgical techniques, and their training should thus focus primarily on mastering the technical side of the novel type of intervention: the robotic console, joysticks, and manipulators. This group benefits from their existing surgical skill set, making the learning process more streamlined.
Contrarily, for novice surgeons who are beginning their surgical career starting with RAS, the learning curve undoubtedly includes both achieving the basic surgical skills as well as technical proficiency with the robotic system. These surgeons must simultaneously learn to navigate the 3D visualization system, to develop hand–eye coordination for laparoscopic instrument maneuver, and to understand the tactile limitations imposed by the lack of haptic feedback in some robotic systems, while constantly learning the surgical intervention itself. This dual learning process can be much more intensive and time consuming compared to that of surgeons already experienced in laparoscopy.
It should be noted that younger generations of surgeons may find transitioning to robotic consoles more intuitive due to their familiarity with and everyday experience of modern technology. This generational comfort with advanced technological interfaces can facilitate quicker adaptation to robotic systems, thus potentially reducing the overall learning curve.
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Rapid mastery of the 3D navigation system is crucial, as all surgical team members must effectively interpret the visual output and translate it into accurate depth perception during procedures. Mastering spatial and depth relationships within the 3D navigation system, coupled with exceptional hand–eye coordination, is paramount for precise instrument manipulation.
Furthermore, achieving proficiency in telemanipulating the robotic system is essential for trainees to maximize surgical precision and minimize complications. Inappropriate force application, for instance, can lead to unintended tissue or blood vessel damage. Mastering instrument movement and interpreting visual feedback represent the initial and most significant hurdles in telemanipulation proficiency. Acquiring this expertise often requires thousands of hours of practice and participation in hundreds of procedures.
Another limitation is due to technological constraints—the lack of haptic feedback in some robotic systems. Tactile sensation, a cornerstone of conventional surgery, allows surgeons to assess tissue resistance. Unfortunately, this crucial feedback mechanism is often absent in robotic surgery, thus forcing surgeons to rely solely on visual assessment. This, combined with potential difficulties in interpreting the visual output, creates a significant obstacle for trainees. How can a recent graduate safely perform robot-assisted surgery without proper depth perception and understanding of tissue consistency?
Teaching during robot-assisted surgery presents its own challenges. While dual-console setups offer trainees the opportunity to gain familiarity with procedures under expert supervision, maintaining control in such scenarios can be more demanding compared to traditional techniques, particularly when the supervising surgeon needs to swiftly regain control in the event of a complication.
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Technical hurdles extend beyond visualization, instrument manipulation, and haptic feedback. Surgeons must also be equipped to address mechanical failures, system crashes, basic troubleshooting, connectivity issues, and user interface warnings and caution messages. While in-depth knowledge of every technical aspect may not be feasible, surgeons should be familiar with common issues and possess the ability to take decisive action during critical system failures, potentially including manual conversion to traditional surgical techniques.
Finally, patient safety remains a major concern in robot-assisted surgery. Comprehensive training, encompassing simulation-based exercises and supervised practice, is mandatory before surgeons perform procedures on live patients. These training steps ensure that surgeons have acquired sufficient experience to safely operate on patients.
Addressing these significant challenges is critical for ensuring the safety and wellbeing of supervisors, operators, and, ultimately, patients. Table 1 provides a summary of challenges in robot surgery education, with explanations.
Table 1
Challenges associated with implementing robot-assisted technology from the educational perspective
Challenge
Explanation
Benefit
Steep learning curve
Initial difficulty in mastering robotic systems and 3D visualization
Leads to enhanced precision and easier performance of complex minimally invasive procedures
Transition for experienced surgeons
Experienced laparoscopic surgeons need to adapt to the technical aspects of RAS
Leverage of existing surgical skills, making it quicker to master robotic systems
Learning for new surgeons
New surgeons need to learn both basic surgical skills and RAS simultaneously
Younger surgeons are generally more familiar with technology, thus easing console learning
Impact of robotic-assisted surgery on learning laparoscopy
The increasing presence of robotic assistance devices in surgical theaters raises important questions about the future of training residents with simple laparoscopic techniques. As more complex procedures are performed with robotic assistance and because younger generations tend to prefer newer technologies, there is a risk that young surgeons may have fewer opportunities to practice conventional laparoscopy interventions, especially in modern, technologically advanced hospitals. Conventional laparoscopy remains an essential skill in many surgical settings.
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Traditionally, the surgical training process has been progressive—from open surgery to laparoscopic techniques before advancing to robotic surgery interventions. However, with the rapidly growing dominance of RAS, this traditional pathway may shrink in the near future, leading to potential gaps in laparoscopic skills among newly graduating surgeons. This shift poses a critical question: how should surgical education balance training in both robotic and conventional laparoscopic techniques?
One of the potential solutions to this problem is an integration of RAS courses as early as possible into surgical education, with adaptation to the most suitable advanced technological interfaces for younger surgeons. This early adaptation to RAS must be complemented (ideally before RAS) by comparably comprehensive training modules on conventional laparoscopic techniques—this will ensure that newer generations of specialists will develop a sufficient skill set in both conventional and technology-assisted surgery. In the case of medical residency programs, the structure of the syllabus should include both approaches—after more critical manual training, a RAS coursework transition would provide a smooth adaptation to robotic technology. This approach ensures that trainees gain proficiency in both techniques, thereby preparing them for a wide range of surgical scenarios.
Moreover, simulation-based surgical practice can be especially beneficial in terms of reducing the barrier between robotic and conventional laparoscopy. Highly advanced laparoscopic simulators can provide realistic hands-on experience without the need for in-human intervention. The integration of laparoscopy simulators into the surgical residency curriculum would certainly enhance the surgical skills related to manual laparoscopy.
It is also worth mentioning that currently, experienced surgeons have mostly experienced not only robotic laparoscopy but also manual laparoscopic techniques and traditional open surgery approaches. Via professional mentoring from broadly experienced clinical professors, young surgeons can benefit from the expertise of their mentors, gaining insights into the practical applications and advantages of both robotic and laparoscopic surgeries. Hence, a balanced approach to minimally invasive surgery can be accomplished by ensuring that the aforementioned steps are accomplished.
Virtual reality and simulator training
Surgical training consoles are nowadays standard equipment for RAS workstations. These consoles allow for effective educational and proficiency development of surgeons in RAS. These systems are designed to simulate the real-world environment of robotic surgery, thus providing trainees with hands-on experience that closely mimics actual surgical procedures.
The surgical training consoles are nearly identical to the surgical console used in the operating room, including the control joysticks, foot pedals, and stereoscopic 3D display. This setup allows trainees to familiarize themselves with the equipment they will use during real surgeries.
Training on actual surgical consoles provides a highly realistic experience that closely mimics real-world surgery scenarios. This hands-on practice helps younger surgeons to develop the muscle memory and hand–eye coordination required for robotic surgery.
Virtual reality (VR) technology allows users to interact with simulated environments through computer-generated visuals [20]. VR typically utilizes goggles or headsets to provide visual output and enables user navigation and manipulation within these environments using specialized devices. This technology has opened doors for novel training methods in robot-assisted surgery.
VR offers a relatively user-friendly training platform. Trainees can benefit from risk-free practice in simulated surgical scenarios. This allows them to develop essential skill sets, including robotic arm manipulation. VR is also valuable for experienced surgeons who can rehearse complex procedures, particularly those involving challenging patient anatomy [21, 22]. Prolonged practice in VR environments can enhance muscle memory, leading to improved hand–eye coordination during actual surgeries.
Furthermore, high-quality VR simulations provide a realistic surgical experience by replicating the operating theater environment, including surgical instruments and visual output. This immersive experience is particularly beneficial for honing EndoWrist manipulation skills, such as grasping and clutching instruments, adapting to 3D visualization, and manipulating instruments and camera tools. Haptic feedback functionality in some VR simulators further enhances realism by allowing trainees to experience tissue resistance. Several commercially available VR simulation workstations cater to robot-assisted surgical training, including the da Vinci Skills Simulator (Intuitive Surgical, Sunnyvale, California), the dV-Trainer, the Flex-VR Trainer, and the RobotiX Mentor (Surgical Science, Gothenburg, Sweden).
The da Vinci SimNow, designed for the da Vinci Surgical System, serves as an example of such VR training platforms. This software offers multiple learning pathways designed for surgeons, physicians, operating room staff, residents, and fellows. Users can select various learning techniques, such as case observation (remotely observing surgical procedures) or virtual simulation sessions practicing diverse surgical scenarios. Additionally, in-center training under the supervision of experienced surgeons is available [23].
Despite its advantages, VR and console simulation for robot-assisted surgery training is not without limitations [24‐27]. The cost of acquiring simulator setups can reach thousands of dollars, and adapting older hospital or university facilities to accommodate VR systems can incur additional expenses. While newer facilities can integrate VR training rooms during initial construction, retrofitting existing buildings can be costly. Furthermore, licensing fees for advanced training software are often not included in the initial purchase price of VR simulators.
Another limitation is the potential for incomplete real-world scenario replication in VR simulations. Technical failures and emergency complications, such as vessel ruptures due to excessive force application, may not be adequately represented. While some VR simulators incorporate haptic feedback, the transition to robotic surgery systems lacking this feature can create adaptation difficulties for trainees. Additionally, VR training primarily focuses on technical aspects of surgery, potentially neglecting crucial elements like communication, decision-making, and teamwork, which are fundamental to successful surgical outcomes [28].
Prolonged VR training sessions can also lead to stress, psychological burnout, or exhaustion. Training regimens should be individually tailored to a trainee’s physical and mental capacity to prevent negative mental health consequences, while ensuring that sufficient surgical experience will be gained from each session. Implementing short breaks between training sessions can further enhance overall learning outcomes [29]. Trainees should be informed and prepared for these potential psychological factors before commencing VR training.
The long-term effectiveness of VR training in robot-assisted surgery requires further investigation. To maximize learning outcomes, educational institutions need to implement long-term studies evaluating the effectiveness of VR training. These studies should explore the adaptation and modification of clinical scenarios, VR learning methods, and trainee–instructor interactions to optimize skill development for real-world surgical practice [30].
Overreliance on VR simulators during training is a potential risk that academic faculties and VR simulator developers should consider when designing validation processes for VR surgical training. Effective validation can motivate trainees to achieve better outcomes and solidify the position of VR training within surgical curricula. Ultimately, improved VR training will translate to higher-quality surgical procedures and better patient outcomes.
Future VR training systems should explore the integration of artificial intelligence (AI). AI algorithms have the potential to enhance training by providing personalized feedback based on trainee performance. For example, AI can track user movements, analyze training inaccuracies, and provide step-by-step guidance or answer questions during simulations. Additionally, AI can contribute to the development of more realistic training scenarios by adapting them to incorporate unforeseen events that may occur during actual surgery [31, 32].
Finally, to ensure widespread accessibility of VR training platforms, government and local policymakers should explore funding initiatives to equip healthcare centers with these valuable training tools [33].
Cadaveric and phantom training
Cadaveric and phantom training offer alternative methods for surgeons to acquire robotic surgery skills. Cadaveric training involves practicing surgical procedures on human cadavers, while phantom training utilizes synthetic models designed to mimic specific tissues or organs.
Cadaveric training has the advantage of being comparable to the living human body. The texture, consistency, and response of cadaveric tissue to surgical instruments closely resembles that of live tissue, providing a more accurate training experience compared to VR or phantom models. Additionally, cadavers possess fully anatomical structures, unlike phantoms with their potentially simplified representations. This inherent anatomical variability exposes trainees to a wider range of challenges, replicating the unpredictable nature of real-world surgery. Cadaveric training also fosters the development of crucial surgical teamwork and planning skills through collaborative practice sessions.
Surgeons can improve their skills in handling delicate tissues, maneuvering instruments with optimal precision, and avoiding potential complications arising from tissue damage during cadaveric training. The step-by-step nature of this training, encompassing proper tissue placement, surgical port selection, suture manipulation, and surgical closure, provides a more realistic progression compared to VR simulations. This translates to a smoother transition to actual surgery with minimal adaptation required from a virtual environment. Cadaveric training remains the gold standard for acquiring proficiency in robot-assisted surgical procedures.
However, cadaveric training presents its own limitations [34‐36]. Ethical considerations are paramount, as human cadavers require respectful treatment. Obtaining cadavers necessitates informed consent, which can be challenging to secure from a donor’s family after the donor’s passing. This contributes to the limited availability of cadavers for training purposes [37]. Furthermore, compared to VR or phantoms, cadaveric training necessitates proper storage facilities, preservation techniques, and disposal procedures, all of which add logistical and cost burdens.
Phantom training offers a more readily available and cost-effective alternative to cadaveric training. The widespread availability of commercially produced phantoms simplifies access to training materials. However, phantoms may not provide a sufficiently realistic representation of human tissue anatomy.
In conclusion, both cadaveric and phantom training represent valuable methods for developing skills in robot-assisted surgery. Cadaveric training provides a highly realistic experience, while phantom training offers greater accessibility and cost effectiveness. The optimal training approach may involve a combination of these techniques to leverage the strengths of each.
Education curriculum
Formal educational programs incorporating RAS courses and workshops are rapidly becoming a basis of surgical training [38, 39]. These structured programs provide a well-defined learning path for surgeons transitioning to this innovative technology. Ideally, instructors for these programs should possess extensive experience with robotic surgery or a deep understanding of the technical aspects of these surgical systems.
The curriculum itself should be a multimodular journey, equipping trainees with the necessary knowledge and practical skills to confidently perform robotic procedures. This typically involves three key components:
Theoretical foundations: This introductory module lays the groundwork by familiarizing trainees with the technical workings of surgical robots. It covers topics such as troubleshooting techniques, interpreting cautionary warnings, safety protocols, and the unique functionalities of these systems. This foundational knowledge is essential before progressing to practical training.
Hands-on practice: The practical module forms the core of the curriculum. Here, trainees gain proficiency in essential skills like robotic arm manipulation, mastering the computer interface, patient preparation, and surgical maneuvers specific to the robotic platform. This is where eye–hand coordination and adaptation to the 3D visual navigation system become essential.
Case-based learning: Having acquired a solid theoretical foundation and sufficient practical skills, trainees culminate their education with case studies. These real-world scenarios allow them to apply their knowledge and decision-making skills to plan and execute surgical interventions collaboratively with other team members. This comprehensive approach effectively validates their medical, surgical, technical, and planning abilities while fostering teamwork—all crucial for success in the operating room.
The increasing popularity of these educational programs addresses the critical need to bridge the gap between theoretical knowledge and practical application of RAS. Standardized comprehensive training is required to ensure patient safety, and these programs are continuously evolving to accommodate advancements in robotic surgical technology. While these structured programs offer significant advantages, there are still hurdles to overcome:
Accessibility: The cost associated with running training facilities and employing expert instructors can be a barrier. Additionally, geographically dispersed programs may necessitate travel, thus creating logistical challenges for some trainees.
Training duration: Courses lasting less than a week may not provide sufficient experience and confidence for independent surgical practice. A learning curve is inevitable when transitioning from conventional to robotic surgery, and a well-structured curriculum should account for this by incorporating an appropriate training duration.
Looking ahead, several promising trends offer exciting possibilities for the future of RAS education:
Integration of artificial intelligence: AI-powered systems hold immense potential for personalizing feedback, analyzing trainee performance metrics, and offering step-by-step guidance during training simulations.
Remote surgical training: Telepresence technologies could revolutionize surgical education by enabling remote mentorship and training opportunities, thus improving accessibility for geographically distant trainees.
National educational pathways: Standardization of curricula implemented during residency or fellowship programs can ensure consistent training quality across institutions, thus fostering higher proficiency amongst graduates.
Online learning resources: A wealth of online resources like lectures, webinars, tutorial videos, and interactive platforms can provide flexible, self-paced learning opportunities to complement practical training and are particularly beneficial for busy surgeons with limited time [40, 41].
By embracing these advancements and addressing existing challenges, we can create a robust educational framework that empowers surgeons with the skills and knowledge to safely and effectively utilize robot-assisted surgery, thereby ultimately improving patient outcomes.
Assessment and certification
Assessment and certification form the final step of comprehensive RAS education programs. These crucial steps aim to establish standardized criteria and a validation process for surgical robot training. Developing objective assessment programs goes beyond simply determining a trainee’s performance. These programs provide comprehensive feedback, identifying areas where specific skills require further improvements. The primary goal of validation is to ensure that trainees have acquired sufficient experience during the training process to safely perform actual robotic interventions, thus minimizing potential patient complications.
Standardized national validation programs could offer a unified approach through theoretical and practical examinations. Some proposed metrics include clinically relevant performance metrics (CRPMs), proficiency-based progression (PBP), and the robotic objective structure assessment of technical skills (R-OSATS) [42‐44]. These standardized assessment tools allow for objective evaluation of trainee performance and ensure mastery of specific areas before proceeding to more advanced training levels [45‐47].
For example, the PBP program, which was implemented 25 years ago, aims to enhance the patient safety and effectiveness of surgical procedures [48]. This can be done by presenting clear and measurable proficiency criteria before entering further steps of the training syllabus. This system is based on
Defined proficiency levels, which are clear, and measurable regulations stating when a given skill has been mastered.
A modular training program, which allows participants to focus on either one or multiple skills. Achieving proficiency in each module is mandatory.
Outcome-based assessments, which allow for constant evaluation of a trainee’s progression. Without achieving the minimum level of proficiency, a trainee cannot advance further in the training modules.
An individualized learning pace, which allows learning at an adjusted pace. Some skills will require less time, others more.
Feedback from the mentors, which is an integral part of training. This aspect provides strengths and weaknesses to the trainee, which still need improvement.
Validation exams could be coupled with the issuance of certificates. These documents serve as a testament to a surgeon’s competence in performing robotic surgery interventions, gaining recognition of these skills at regional, national, or even continental levels. The designation of “board-certified robotic surgeon” after rigorous training and examinations signifies the quality of performed procedures. National credentialing programs further enhance the recognition and credibility of acquired skills, thereby fostering increased trust among patients who might be apprehensive about robotic surgery.
Currently, credibility validation programs are in their infancy. There is a global absence of standardized examinations with certifications, and potential unification of international programs remains a hurdle. Furthermore, considering the rapid pace of technological development and the diverse range of robotic devices available, surgical training programs may not be readily established across all surgical fields. Governments might initially prioritize specializations like cardiac and emergency surgery.
The literature contains numerous examples of academic robotic surgery curricula. A systematic review by Khan et al. analyzed existing robotic surgical training modules within academic surgery programs [39]. Their findings highlight a current dominance of short-duration commercial programs, with less emphasis on supervised surgeries. Significant variation was reported amongst the reviewed studies, resulting in a lack of uniformity across course structures. This heterogeneity contributes to the fact that most current curricula fall short of the requirements for standardized training programs.
Universities are increasingly incorporating robot-assisted surgery courses into their curricula. Institutions such as University College London, AdventHealth University, Anglia Ruskin University, and the Washington University School of Medicine offer master’s degree programs that encompass robotic navigation, planning, control, surgical data acquisition, hands-on robotic training, simulations, and real-world scenarios [49‐51].
By addressing the existing challenges and embracing advancements like standardized validation and university-level education, we can create a robust educational framework in RAS. This, in turn, will empower surgeons with the necessary skills and knowledge to confidently and effectively utilize robotic technology, ultimately leading to improved patient outcomes.
Artificial intelligence
Artificial intelligence, encompassing subfields like machine learning (ML) and deep learning (DL), represents a collection of computational algorithms designed to automate tasks traditionally requiring human cognitive abilities. AI achieves this automation through problem-solving, natural language processing, decision-making, and pattern-recognition techniques [52]. These advancements hold significant promise for revolutionizing surgical education, particularly in the rapidly evolving field of robot-assisted surgery.
Language-based AI models, such as ChatGPT‑4, offer various possibilities for interactive learning experiences. Trainees encountering procedural uncertainties could interact with these models to gain clarity on complex concepts. AI-powered systems could provide valuable support by generating [53]
Concise learning materials: Point-by-point summaries can be automatically generated, facilitating the comprehension of intricate surgical procedures.
Visually informative graphics: Key concepts can be illustrated through clear and informative graphics, enhancing knowledge retention.
Targeted literature recommendations: AI algorithms can recommend relevant research papers for further exploration, fostering a deeper understanding of specific surgical techniques.
Intelligent tutoring systems: These educational programs powered by AI can extract critical data from various surgical maneuvers, providing the trainee with feedback, and adjust multimedia instructional material to improve areas in which the trainee has not reached the desired requirements [54, 55].
The widespread availability of free, AI-powered platforms can democratize access to high-quality surgical education. This can potentially bridge geographical divides, allowing millions of students worldwide to benefit from readily accessible guidance in this evolving field [56].
The convergence of AI and virtual reality technologies represents another development. Osso VR (Osso VR, San Francisco, California) exemplifies this innovative approach, creating highly realistic surgical scenarios for immersive training experiences [57]. Similarly, the Lapp Simulation Training Workstation (Surgical Science, Gothenburg, Sweden) utilizes AI to provide personalized feedback on trainee performance, thereby identifying areas requiring improvement. Integration of AI within VR simulators has the potential to revolutionize RAS training by offering a more sophisticated and data-driven learning environment.
The potential of AI extends beyond the realm of education and into the operating room itself. Real-time AI support during surgery offers significant promise for enhancing surgical decision-making:
Providing evidence-based guidance: AI can offer real-time guidance based on vast datasets and best practices, aiding surgeons in their decision-making processes.
Surfacing relevant clinical trial data: Access to pertinent results from randomized trials can further inform surgical decision-making, particularly in complex cases with variable anatomy or uncommon emergencies.
Video labelling: AI can be used to quickly analyze critical moments from video recordings of the surgical simulation. This feedback remains invaluable for constant improvement of trainees.
While the integration of AI offers enormous possibilities, it is crucial to acknowledge potential risks [58]:
Bias in AI algorithms: AI systems can inherit biases from the data they are trained on, potentially influencing surgical decision-making in unintended ways. Mitigating bias through careful data selection and algorithm design is crucial. AI algorithms should be adjusted to prevent underskilling and overskilling a surgeon’s performance [53].
Data limitations: Training data may not fully capture the nuances of real-world surgical scenarios, thus leading to potential discrepancies. Utilizing diverse datasets is essential for ensuring generalizability of AI-powered surgical education and decision support tools. Many studies have reported use of AI techniques; however, they have not implemented external validation, which is crucial to determine the effectiveness of machine learning algorithms.
Accountability for AI errors: In the event of complications arising from AI-assisted training or decision-making, assigning responsibility becomes a complex issue. Clear ethical guidelines and frameworks are needed to address potential liability concerns.
Validity and transparency: Ideally, to perfectly understand surgical training errors, a trainee needs to receive a clear, step-by-step feedback. The way in which AI analyzes the surgical training procedure is a cornerstone of full integration into the training. Lack of transparency of AI analysis may discourage policymakers and surgeons from implementing this option.
Biased gold standard: AI algorithms and their metrics are mainly evaluated against human expert decisions; however, expert opinions may differ, thus creating variations and biases among AI models.
Surgeons must remain vigilant, critically analyzing AI-generated information and applying their own clinical expertise. Table 2 shows the potential and challenges of AI in surgical education and practice.
Table 2
Potential and challenges of AI in surgical education and practice
AI application
Potential
Challenges
Personalized feedback in training
Tailored guidance based on performance metrics; improved learning outcomes
Requires diverse and representative training datasets to avoid bias
Enhanced simulation environments
Dynamic and adaptive scenarios; exposure to rare and complex cases
High development and implementation costs; may not capture all real-world nuances
Automated mentorship and support
On-demand assistance and learning materials; democratizes access to high-quality resources
Ensuring data privacy and security; maintaining accuracy and reliability
Real-time decision support
Evidence-based recommendations during surgery; improved decision-making
Accountability and liability concerns; potential overreliance on AI systems
Predictive analytics
Anticipates surgical outcomes and complications; guides postoperative care
Data limitations; ensuring generalizability across diverse clinical scenarios
Integration complexity; requires high-quality imaging data
Discussion
The rapid development of robots in surgery necessitates a parallel evolution in surgical education. This comprehensive review explores the current state of robot-assisted surgery (RAS) education, analyzing its limitations, existing applications, and promising future directions.
While RAS offers undeniable advantages, its implementation presents several challenges that curriculum development must address, as outlined in Table 3 [59, 60]. A significant hurdle is the steep learning curve for surgeons transitioning from traditional techniques. Mastering the novel computer interface, 3D visualization system, and telemanipulation techniques requires dedicated training. Additionally, the current lack of real-time force feedback in many robotic systems hinders depth perception and tissue consistency evaluation, impacting the ability to navigate effectively during procedures. Furthermore, understanding the intricacies of robotic systems, including troubleshooting warnings and malfunctions, necessitates additional technical knowledge beyond the standard medical school curriculum.
Table 3
Comparison of training methods in surgical education
High cost; may lack complete realism; potential for psychological strain
Simulator training on real consoles
Provides real clinical experience; familiarity with actual surgical systems
Requires access to physical consoles; high equipment cost
Cadaveric training
Realistic anatomical experience; valuable for teamwork development
Ethical concerns; limited availability; high preservation costs
Phantom training
Cost-effective; readily available; useful for initial skill development
May not fully replicate human tissue anatomy
To address these challenges, various educational strategies have emerged. Virtual Reality simulators offer a relatively novel, risk-free environment for developing surgical skills and practicing robotic procedures. High-quality graphics create a realistic experience; however, costs, VR adaptation difficulties, potential psychological strain, and limited real-world scenario representation pose limitations. Cadaveric training provides a more realistic environment for practicing procedures and encountering anatomical variations. It fosters teamwork skills as well. However, ethical considerations, logistical challenges related to cadaver preservation, and limited availability remain drawbacks.
Comprehensive training programs should ideally combine various elements to optimize education. Didactic sessions provide foundational knowledge on RAS principles and procedures through lectures. Hands-on workshops enable trainees to practice specific skills under expert guidance. Supervised practical training allows trainees to apply their knowledge and skills in a controlled setting. This blended approach ensures that trainees acquire the necessary knowledge and technical expertise to perform RAS procedures confidently.
The future of RAS education holds exciting possibilities. Integration of artificial intelligence could personalize VR simulation training by providing feedback, movement analysis, and real-time guidance. Additionally, AI could introduce more comprehensive clinical scenario features. Standardized learning pathways through national-level curricula implemented during residency or fellowship training could ensure consistent training quality across institutions. Developing objective validation programs to assess RAS simulation training is crucial for patient safety. Advanced systems like the da Vinci 5, which allows surgeons to track their performance metrics against benchmarks, can motivate continuous skill improvement.
The growing adoption of RAS necessitates a robust educational framework to equip surgeons with the necessary skills and knowledge. By acknowledging current limitations, embracing innovative approaches like AI and VR, and prioritizing standardized curricula, we can ensure the safe and effective integration of RAS into surgical practice, ultimately improving patient outcomes.
Conclusion
Robot-assisted surgery is revolutionizing the operating room, but surgeon education needs to keep pace. Challenges include a steep learning curve for new technology and a lack of haptic feedback in some systems. Fortunately, innovative educational strategies are emerging. VR simulation offers a safe and realistic training environment, while cadaveric training provides a more hands-on experience. The future holds promise for AI-powered VR simulations with personalized feedback and standardized national curricula. Ultimately, by acknowledging limitations and embracing new technologies, we can ensure that surgeons have the skills to safely integrate RAS and improve patient outcomes.
Conflict of interest
P. Łajczak, J. Janiec, K. Żerdziński, K. Jóźwik, P. Nowakowski, and Z. Nawrat declare that they have no competing interests.
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