ReviewSurgical endoscopy2026
Status of the application of artificial intelligence (AI) in robotic surgical skills training in the modern robotic era and future horizons: a systematic review.
Review in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is a rapidly evolving field with increasing applications in surgical education and assessment; however, its integration into robotic surgical skills training remains in the early stages. The aim of this systematic review is to evaluate the current state of AI applications in robotic surgical skills training and outline future research directions for integrating AI into robotic surgical skills training.
methodThis review was reported in accordance with the PRISMA 2020 statement. A systematic search was performed across PubMed, Scopus, Web of Science, the Cochrane Library, and Ovid Embase. Studies were eligible for inclusion if they applied artificial intelligence (AI) methods to robotic surgical skills training, were published in English, and were published between 1 January 2000 and 1 October 2025. Risk of bias was assessed using the Cochrane RoB 2 tool and the ROBINS-I tool.
resultOf the 4400 studies initially identified, 36 met the inclusion criteria and were included in the final analysis. Among these, 12 were assessed as having a low risk of bias, while 24 were judged to present some concerns; studies with a high risk of bias were excluded. All included studies employed AI-based platforms, with the Da Vinci Skills Simulator (dVSS) being the most frequently used system. AI methods were applied to support surgical skills development, with Suturing and Needle Passing emerging as the most evaluated tasks. AI-based feedback, AI training methodologies and models, and AI-based assessment are also employed.
conclusionAI-based simulators and training methods have been developed for robotic surgical skills training and are increasingly used to enhance the technical abilities of trainees in robot-assisted surgery (RAS). However, the integration of AI and robotic surgical skills training is still at an early stage. Lack of standardized implementation and limited integration of AI tools into formal robotic surgical skills training continue to hinder broader adoption.
trial registrationThis systematic review was registered with PROSPERO, registration number is CRD420251178615.
Indexed as
Identifiers
42806086What OpenQuestion holds
Registered trials
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.