Mimic dV-Trainer is the first developed simulator and the one with the most validation studies. Its prototype was introduced in 2007. Mimic dV-Trainer is a stand-alone, portable, tabletop device with mobile foot pedals. This device has 65 unique exercises ranging from basic to advanced. Users’ hand and wrist movements are tracked with three cables, which is different from the RoSS and the real da Vinci robot console. Its own evaluating system enables the learners to keep track of their performance by showing their scores and errors for both individual metrics and the whole task. The system has proven its applicability for robotic surgery training, showing the correlation between the performance of virtual reality and the performance on the real da Vinci robot console. Nine different studies have corroborated this [16–24] (Table 35.1). Training on the Mimic dV-Trainer has similar effects in basic skill improvement as the training in the setting of dry laboratory. Daily training on the dV-Trainer system has been recommended; 1 h a day for four consecutive days has shown greater improvement in skill [25].

This simulator has also been shown to be useful to acquire advanced skills in certain procedures [26]. Also, Lendvay et al. demonstrated that practicing on the mimic dV-Trainer 3–5 min prior to the surgery improves the time of surgery and the economy of the movement [27].

RoSS is a portable, stand-alone system that has been available since 2009. It provides 52 unique exercises organized into five categories: module orientation, motor skills, basic surgical skills, intermediate surgical skills, and a surgical training. The RoSS has its own hardware, which differs from the current da Vinci Surgical System mainly in hand controls, having a lower range of movement resulting in a greater need for clutching.

RoSS system has been proven to be a useful training tool for developing robotic surgical skills [28]. Stegemann et al. suggested that by practicing on the RoSS system, surgeons can gain better surgical skills. Also the study advocated that the implementation of RoSS simulator practicing into a standardized training program results in significant improvement in the basic skills of robotic surgery [29]. The curriculum, formally known as the Fundamental Skills of Robotic Surgery (FSRS), consists of 16 RoSS tasks from four modules: basic console orientation, psychomotor skills training, basic surgical skills, and intermediate surgical skills [28].

RoSS has the ability to measure several performance metrics [30–33] (Table 35.1). Chowriappa et al. developed an evaluating system, Robotic Skills Assessment (RSA) Score, in an effort to delineate real-world performance metrics from others [33]. This scoring system provides the users a valid and standardized assessment tool for reality virtual simulation. A panel of robotic surgery experts developed the score by defining tasks, assigning weights, and integrating performance metrics into a hierarchical scoring system. They gave more importance to the surgical safety and critical errors but less to the time of completion of the tasks. Evaluation of RoSS system was later on based on the RSA system. It was applied to compare the scores of novice and expert surgeons to confirm its construct validity. The RSA scoring system is potentially applicable to all robotic virtual reality simulators.

dVSS is the only simulator directly connected to the console of the da Vinci Surgical System. It was first introduced in 2011 and embraces 40 exercises. There are no discrepancies in the hardware; however, the simulator cannot operate independently, requiring a console of the da Vinci Surgical System. A disadvantage of this simulator is that if the da Vinci Surgical System is in clinical use, the dVSS is then not able to be used for training purposes.

The dVSS is a useful training tool that has been widely studied, including face, content, construct, and predictive validity studies [9, 34–39] (Table 35.1). In Culligan et al. predictive validity study, surgeons performed better in robotic hysterectomy cases after training on dVSS [40]. Hung et al. demonstrated that baseline skills on the dVSS were predictive of baseline and final scores on da Vinci ex vivo tissue performance [41].

Several research groups established and validated their training programs; three research studies demonstrated proficiency-based training curriculum [40, 42, 43]. Bric et al. established an expert proficiency level: three consecutive scores at or above this level is considered to be proficient [42]. Culligan et al. also adopted the expert proficiency levels, but did not comment if consecutive attempts were required [40]. Zhang et al. used 91% composite score as the standard for proficiency [43].

Two research groups introduced their training programs , which were based on the completion within maximum number of attempts. Gomez et al. [44] and Vaccaro et al. [45] described curriculums by achieving the global score of 80% within a maximum of six and ten attempts, respectively.

University of Southern California (USC) conducted two studies. One was the concurrent and predictive validity study of the dVSS in the setting of ex vivo tissue laboratory, showing significant performance improvement from the baseline after practice [41]. The other study was the correlation study between the training on the simulator and the clinical performance of residents and fellows [29].

After completing the preclinical training stage, the surgeon can begin the clinical phase, which involves direct contact with an actual patient.

Each of the following training models facilitates the development of surgical skills.

The inanimate exercises model is one of the more economical models, thus having the advantage of being accessible and allowing for proper introduction to robotic procedures.

The tissue laboratory model is also a low-cost, easily accessible model. It allows for development of skills specific to a particular point in a surgical procedure. However, this model does not utilize newer technologies.

The animal or cadaveric model is the best model for training, allowing for low to high complexity skills development and the possibility for simulating real-time handling of intraoperative complications. The biggest disadvantage is that these models are difficult to initiate and sometimes simply banned depending on the laws in the country of use.

The virtual reality model is a costly method (40,000–100,000 USD) but can be considered as the model with the best cost-benefit ratio since it allows low to medium complexity skills development without the robotic console. The virtual reality model simulates the interface of the surgeon console and is available to the training surgeon more conveniently. There have been no studies indicating which robotic console training method represents the best option.

An expert surgeon is a person who has acquired knowledge and surgical skills through experience and instruction. There have been various mechanisms described to achieve this status: the first and very controversial is the acquisition of expert status through amount of training hours [48], as shown in some studies, including that conducted by Korets et al. [17]. It analyzed the execution of some specific surgical exercises comparing three groups: group one had incomplete training, group two had complete training, and group three was the control group without training, which demonstrated the two groups that had training had better surgical ability.

The duration of training and interval between training sessions needed to improve skills have not been stipulated; however, there have been studies trying to determine these parameters, such as that of Kang et al. [25]. The study compares three training regimens: 1 h daily for four consecutive days, 1 h weekly for four consecutive weeks, and four consecutive hours in 1 day. The group that trained for 1 h daily for four consecutive days was associated with increased performance and continuous score improvement.

Another important model in skills training is problem-based learning (PBL) , an instructional method in which students learn through facilitated problem-solving. The main objectives in this model are to acquire (1) flexible knowledge, (2) problem-solving skills, (3) skills in self-directed learning, (4) effective collaboration skills, and (5) intrinsic motivation. This model shows that complex problems do not have a single correct answer [49].

With the many existing models of robotic surgery training in mind, the University of Southern California proposes a model based on two phases: first, the preclinical phase, and second, the clinical phase [1] (Fig. 35.1). A similar model was developed and validated for the realization of robot-assisted radical prostatectomy (RARP) by Volpe et al. [50]. They showed that a 12-week curriculum, which included 1 week each of structured simulation-based training; e-learning or virtual reality training; synthetic, animal, and cadaveric platforms; and supervised modular training for RARP, is feasible, valid, and impactful on surgical education. The participants in the RARP training improved their basic robotic surgical skills and their capacity to carry out the preclinical training into the clinical phase of RARP. Recently, Lovegrove et al. developed and validated the Healthcare Failure Mode and Effect Analysis (HFMEA) , a safety and assessment tool to measure the technical skills of surgeons performing robot-assisted radical prostatectomy. HFMEA, which supervises improvement and measures progress, can be used in the future to guide mentors to allow their training surgeons to perform procedures safely.