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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>Of 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>This systematic review was registered with PROSPERO, registration number is CRD420251178615.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00464-026-13396-y","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:30:00Z","timestamp":1790613000000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Status of the application of artificial intelligence (AI) in robotic surgical skills training in the modern robotic era and future horizons: a systematic review"],"prefix":"10.1007","author":[{"given":"Shuntong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyou","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjie","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"13396_CR1","doi-asserted-by":"crossref","unstructured":"Morrell ALG, Morrell-Junior AC, Morrell AG, Mendes JMF, Tustumi F, DE-Oliveira-E-Silva LG, Morrell A (2021) The history of robotic surgery and its evolution: when illusion becomes reality. 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