AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 22 Sep 2026] Title:Lessons learned from deploying imaging AI with the open PACS-AI platform View a PDF of the paper titled Lessons learned from deploying imaging AI with the open PACS-AI platform, by Samuel Kadoury and 13 other authors View PDF Abstract:We describe deploying imaging AI at six hospitals through PACS-AI, an open self-hosted platform. The binding constraint is not model accuracy but infrastructure to route studies, display results, capture feedback, and audit what runs. At one center, angiography models completed 515 of 607 jobs (84.8%); failures reflected absent diagnostic views, and 78.1% of 638 clinician ratings were positive. Publishing honest readiness levels for every model is itself a governance practice. Comments: 28 pages (21 main text + 7 supplementary), 3 figures, 1 table Subjects: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY) Cite as: arXiv:2609.26981 [cs.CV] (or arXiv:2609.26981v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.26981 arXiv-issued DOI via DataCite (pending registration) Submission history From: Robert Avram [view email] [v1] Tue, 22 Sep 2026 19:18:09 UTC (1,921 KB) Full-text links: Access Paper: View a PDF of the paper titled Lessons learned from deploying imaging AI with the open PACS-AI platform, by Samuel Kadoury and 13 other authors View PDF view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)