Evidence map›Paper›PMID 35229215›Full record

ReviewSurgical endoscopy2022

Objective assessment of robotic surgical skills: review of literature and future directions.

Saratu Kutana, Daniel P Bitner, Poppy Addison, Paul J Chung, Mark A Talamini, Filippo Filicori

Abstract readReview
PubMed Publisher
In one paragraph

Review in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Review
  9. Blob representation of robotic surgical scenes for position-aware video generation.International journal of computer assisted radiology and surgery · 2026
    Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication.Global surgical education : journal of the Association for Surgical Education · 2025
    Article
  16. Article
  17. Review
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Saratu KutanaIntraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.
Daniel P BitnerIntraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA. DBitner@northwell.edu.ORCID http://orcid.org/0000-0003-0776-3118
Poppy AddisonIntraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.
Paul J ChungIntraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.
Mark A TalaminiDonald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
Filippo FilicoriIntraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvaluation of robotic surgical skill has become increasingly important as robotic approaches to common surgeries become more widely utilized. However, evaluation of these currently lacks standardization. In this paper, we aimed to review the literature on robotic surgical skill evaluation.

methodsA review of literature on robotic surgical skill evaluation was performed and representative literature presented over the past ten years.

resultsThe study of reliability and validity in robotic surgical evaluation shows two main assessment categories: manual and automatic. Manual assessments have been shown to be valid but typically are time consuming and costly. Automatic evaluation and simulation are similarly valid and simpler to implement. Initial reports on evaluation of skill using artificial intelligence platforms show validity. Few data on evaluation methods of surgical skill connect directly to patient outcomes.

conclusionAs evaluation in surgery begins to incorporate robotic skills, a simultaneous shift from manual to automatic evaluation may occur given the ease of implementation of these technologies. Robotic platforms offer the unique benefit of providing more objective data streams including kinematic data which allows for precise instrument tracking in the operative field. Such data streams will likely incrementally be implemented in performance evaluations. Similarly, with advances in artificial intelligence, machine evaluation of human technical skill will likely form the next wave of surgical evaluation.

Indexed as

RoboticsRobotic Surgical ProceduresArtificial IntelligenceClinical CompetenceHumansReproducibility of ResultsRobotic surgerySkill assessmentSurgical skill evaluation

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.