{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T01:34:08Z","timestamp":1793064848453,"version":"4.5.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n To evaluate the performance of a commercial artificial intelligence (AI) software in detecting intracranial hemorrhage (ICH) in emergency settings, compared to on-call radiology residents.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods<\/jats:title>\n All consecutive unenhanced cerebral CT-scans performed in a single center over a 3-month period in the emergency department in patients with suspected ICH, initially interpreted by radiology residents on-call and subsequently verified and approved by a board-certified radiologist, were concomitantly analyzed by an AI software for the presence of ICH. Results from the AI software were stored in a separate PACS partition and were unavailable to the radiologists for the case reading.<\/jats:p>\n We assessed the diagnostic performance of the AI software and of the radiology residents in detecting ICH. The reference standard was the final report of the board-certified radiologist.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Radiology reports of 2153 CT-scans were analyzed, and ICH prevalence was 15.4% (331\/2153). The AI software achieved an overall sensitivity of 84% and a specificity of 94.4%, and radiology residents achieved a sensitivity of 96.4% and a specificity of 99.6%, respectively (\n p<\/jats:italic>\n -values\u2009<\u20090.001). The sensitivity was 97.7% for AI and 98.5% for residents when CT examinations displayed an association of multiple hemorrhagic types (\n p<\/jats:italic>\n \u2009=\u20091). The sensitivity was 95.2% for AI and 98.4% for radiology residents in the presence of multiple ICH sites (\n p<\/jats:italic>\n \u2009=\u20090.11).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Radiology residents demonstrated a significantly higher performance in detecting ICH compared to the AI software. AI exhibited very good diagnostic performance only in the presence of multiple hemorrhagic sites or multiple hemorrhage types.<\/jats:p>\n <\/jats:sec>\n \n Key Points<\/jats:title>\n \n \n Question<\/jats:italic>\n <\/jats:bold>\n How does the performance of the AI software compare to that of radiology residents in detecting ICH on unenhanced CT in real-life emergency workflow conditions?<\/jats:italic>\n <\/jats:p>\n \n \n Findings<\/jats:italic>\n <\/jats:bold>\n In the emergency setting, the AI software demonstrated lower overall sensitivity and specificity than radiology residents for detecting ICH<\/jats:italic>\n .\n <\/jats:p>\n \n \n Clinical relevance<\/jats:italic>\n <\/jats:bold>\n In real-life emergency conditions at a university hospital, the AI software did not offer a superior performance compared to radiology residents in detecting ICH. The integration of AI in this specific setting remains to be defined<\/jats:italic>\n .\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1007\/s00330-026-12385-y","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T15:58:05Z","timestamp":1771689485000},"page":"5996-6006","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance of AI vs radiology residents in the detection of intracranial hemorrhage on emergency CT: a real-world analysis"],"prefix":"10.1007","volume":"36","author":[{"given":"Quentin","family":"Pedrini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Periktioni Crina","family":"Chrysostomou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Alexandre","family":"Poletti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minerva","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Perneger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodolphe","family":"Meyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1754-8702","authenticated-orcid":false,"given":"Alexandra","family":"Platon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"12385_CR1","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/B978-0-444-53485-9.00022-2","volume":"135","author":"S Rincon","year":"2016","unstructured":"Rincon S, Gupta R, Ptak T (2016) Imaging of head trauma. 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