Robotic-assisted surgery changes the physical and visual demands placed on surgeons. Instead of manipulating instruments directly through small abdominal incisions, the surgeon controls articulated tools from a console, typically while viewing a magnified three-dimensional image. The system can provide greater instrument articulation and fine motion control, but these advantages do not automatically translate into technical competence. Suturing requires coordinated needle handling, accurate tissue bites, controlled tension, and efficient instrument exchanges. A trainee must also work within the constraints of a narrow operative field while maintaining a stable camera view and avoiding unnecessary movements. Laparoscopic experience provides a foundation, yet robotic instruments and the console interface create a different motor environment. The authors therefore treated adaptation as more than simply repeating laparoscopic exercises with a robot. They redesigned the existing tasks for robotic instrumentation and added a non-dominant forehand suturing exercise intended to challenge a less familiar hand position.

The original ATLAS curriculum was developed to train and assess advanced laparoscopic suturing through defined tasks and proficiency standards. Its central idea is mastery learning: learners practice until they meet an objective performance level, rather than stopping after a fixed number of attempts or a predetermined amount of time. This approach can make training more consistent because progress is linked to demonstrated ability. It also allows educators to identify specific technical weaknesses and provide targeted practice. For R-ATLAS, the investigators adapted all six ATLAS tasks to the Intuitive Abdominal Dome Trainer, a simulator designed to reproduce aspects of abdominal surgery. The additional task, called 3ND, required non-dominant forehand suturing. Such a task is technically important because surgeons may need to use either hand depending on anatomy, instrument position, access angle, or the direction of a repair. The resulting curriculum contained seven exercises intended to represent demanding components of robotic suturing.

To establish reference performance, four expert robotic surgeons completed five repetitions of every task using a da Vinci Xi system. Their performances were recorded on video and scored independently. In total, the study collected 140 attempts. Six attempts were classified as outliers because they fell more than two standard deviations from the relevant performance distribution. After those exclusions, 134 attempts remained for analysis. The investigators used descriptive statistics to summarize how the experts performed and to calculate benchmarks for each exercise. This design does not compare novices with experts, and it does not test whether achieving a benchmark improves outcomes in the operating room. Instead, it uses expert performance as a starting point for defining what a high-level simulated performance might look like. The process reflects a common strategy in skills education: first establish a measurable reference range, then examine whether the standard is reliable, teachable, and related to real clinical performance.

Benchmarks were established for all seven R-ATLAS tasks, but the results also exposed substantial differences among the experts. Mean performance varied for Tasks 1 through 5 and for the 3ND exercise. Task 6, by contrast, showed relatively similar performance across the surgeons. This pattern challenges the idea that expertise always produces one narrow, uniform technique. Surgeons can reach a technically acceptable result through different combinations of movement, timing, instrument positioning, and needle control. Variation may also reflect the intrinsic difficulty of a task or the ways in which specialists develop individual strategies over years of practice. For educators, the finding is both useful and cautionary. A benchmark derived from a small expert group can provide a practical target, but it may also encode the particular mix of styles represented in that group. The researchers therefore describe the thresholds as preliminary and call attention to the need for further validation before they are treated as definitive standards.

The study also examined perceived workload using the NASA Task Load Index, a tool that captures subjective demands such as mental effort, physical effort, time pressure, frustration, and perceived performance. Scores ranged from 15.5 to 30 across the exercises. The highest workload was reported for Task 3ND, the non-dominant forehand exercise, while Task 4 produced the lowest workload. These measurements add an important dimension to technical scoring. Two tasks may appear similar when judged by completion time or errors, yet require very different levels of concentration and effort. A high-workload exercise may reveal where trainees are most likely to struggle, even if they eventually complete it successfully. It could also help instructors sequence a curriculum, introduce deliberate practice, or monitor whether repeated training makes a task feel less demanding. Because the workload findings came from the expert sessions, they should not be assumed to represent novice experience, but they identify areas that deserve attention in later studies.

R-ATLAS could eventually support more standardized robotic education by giving programs a shared vocabulary for advanced suturing. A trainee’s progress could be tracked across repeated attempts, with feedback tied to observable performance rather than general impressions. Video recording also creates opportunities for independent review and remote assessment, potentially allowing instructors at different institutions to examine the same technical behaviors. The framework may be especially relevant as surgical training programs integrate robotic procedures while still needing to teach fundamental principles of tissue handling and repair. However, a simulator cannot reproduce every feature of an operation. Real patients introduce variable anatomy, tissue fragility, bleeding, unexpected findings, team communication, and time-sensitive decisions. Meeting a simulated benchmark should therefore be viewed as evidence of performance on a defined exercise, not proof that a surgeon is ready to perform an entire procedure independently.

The authors’ most important message may be that robotic proficiency requires measurement without oversimplification. R-ATLAS supplies an organized platform, seven tasks, expert-derived reference scores, and workload data, but it does not end the debate over what mastery means. Future research will need to test the curriculum with larger and more diverse expert groups, determine how consistently different evaluators score performance, and examine how trainees improve with practice. Studies could also investigate whether benchmark achievement transfers to clinical skills, whether different robotic platforms produce comparable results, and how patient-specific complexity should influence assessment. For now, the study offers a practical bridge between laparoscopic education and robotic surgery. By translating an advanced suturing curriculum into a robotic environment while acknowledging expert variability, R-ATLAS provides educators with a measurable starting point for training surgeons to make precise, controlled movements when the smallest technical details can matter most.

An important feature of the R-ATLAS design is its attempt to preserve the educational logic of the original ATLAS curriculum while changing the interface through which the skills are performed. This distinction is relevant to curriculum design: a robotic simulator can assess suturing mechanics, but the meaning of a score depends on the task’s construction, the platform, and the scoring rules. By adapting the exercises specifically for robotic instruments, the investigators created a platform-focused assessment rather than assuming that laparoscopic standards could be transferred unchanged. The work therefore addresses a practical gap identified in robotic education, where programs have adopted robotic technology faster than they have developed consistent approaches for teaching advanced technical maneuvers.

The benchmark process also illustrates why proficiency standards require ongoing validation. Four experts completing repeated trials can reveal the range of performance expected from highly experienced users, but that sample is not large enough to establish how broadly the thresholds apply across specialties, institutions, training backgrounds, or robotic systems. Removing six predefined outlier attempts reduces the influence of unusually atypical performances, yet it can also narrow the observed distribution if those attempts reflect meaningful variation rather than measurement noise. The resulting scores should consequently be interpreted as provisional estimates derived from this study’s expert sample. Reliability testing, including agreement among independent raters and consistency across assessment sessions, would strengthen the evidence that a trainee’s score reflects skill rather than scoring or testing variability.

The findings are also consistent with a broader principle in simulation-based mastery learning: assessment is most useful when it is connected to deliberate practice and actionable feedback. A numerical threshold can tell an instructor that performance falls short, but it does not by itself identify whether the problem involves needle orientation, tissue handling, economy of motion, or control of the non-dominant instrument. R-ATLAS may become more educationally valuable if future implementations pair its benchmarks with error taxonomies, motion-based measures, or structured video feedback. Such additions could help distinguish a slow but precise learner from a fast performer whose technique creates unnecessary force or inconsistent suture placement.

Clinical transfer remains the central question for any simulator-based benchmark. Prior simulation research cited by the investigators supports the general proposition that proficiency-based training can improve technical performance, but the present study does not demonstrate that R-ATLAS scores predict patient outcomes or operating-room readiness. Establishing that relationship would require prospective studies following trainees from simulator practice into clinical cases and examining outcomes with appropriate safeguards. It would also be important to determine whether repeated practice produces durable retention rather than short-term familiarity with the simulator. Even before those studies are completed, the curriculum offers a research-ready structure for comparing training strategies and for studying how robotic dexterity develops, making its preliminary benchmarks useful as measurement tools as well as educational targets.

Subject of Research: Robotic-assisted suturing training and proficiency benchmarking

Article Title: Robotic ATLAS: adapting advanced laparoscopic suturing training to a robotic platform with proficiency benchmark scores

Article References: Jonas, N., Chen-Goodspeed, A., Yousef, S., Hsu, C.-H., Soliman, D., Nepomnayshy, D., Zheng, J., Ford, H., Nejad, A., Ritter, M., Hodges, J., & Ghaderi, I. (2026). Robotic ATLAS: adapting advanced laparoscopic suturing training to a robotic platform with proficiency benchmark scores. Global Surgical Education – Journal of the Association for Surgical Education, 5(1), Article 171. https://doi.org/10.1007/s44186-026-00572-w

Image Credits: AI Generated

DOI: 10.1007/s44186-026-00572-w

Keywords: Robotic surgery, Surgical education, Laparoscopic suturing, Simulation training, Proficiency benchmarks, R-ATLAS, Surgical robotics, NASA-TLX, Robotic, ATLAS, adapting, advanced