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Using the 1st to 10th editions of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open Data across fiscal years 2014\u20132023, diffusion trajectories were visualized for 21 procedures approved for insurance coverage by 2020 through bivariate plots of annual surgical volume versus RAS replacement rates. RAS diffusion exhibited non-linear dynamics associated with annual procedure volume. In niche markets with &lt;\u20096,000 annual cases (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20099), six procedures demonstrated high growth in RAS replacement rate. Conversely, among 12 procedures with \u2265\u20096,000 annual cases, four did not cross the 16.0% chasm threshold through FY2023, and three required 6 years to surpass it. These patterns were thought to reflect challenges in the transition from specialized centers to general hospitals. Furthermore, a trend consistent with \u201cintra-specialty spillover\u201d was observed, wherein procedures approved in FY2020 achieved higher initial adoption than those in FY2018; although inseparable from the overall expansion of RAS since 2018, this suggests that accumulated infrastructure and expertise may facilitate the uptake of subsequent techniques. These findings suggest that RAS diffusion is associated with annual procedure volume, organizational readiness, and specialty\u2011specific infrastructure alongside technical factors. Future implementation strategies may benefit from infrastructure sharing within and across specialties and resource optimization to bridge the observed diffusion chasm.\n                  <\/jats:p>","DOI":"10.1007\/s11701-026-04049-2","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T01:41:58Z","timestamp":1791769318000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling the Chasm in Japanese robot-assisted surgery diffusion: the interplay of surgical volume and intra-specialty spillover"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9566-9776","authenticated-orcid":false,"given":"Yusuke","family":"Natsume","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6625-2644","authenticated-orcid":false,"given":"Shingo","family":"Kano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"4049_CR1","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11701-019-00956-9","volume":"14","author":"TM Connelly","year":"2020","unstructured":"Connelly TM, Malik Z, Sehgal R et al (2020) The 100 most influential manuscripts in robotic surgery: a bibliometric analysis. 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