{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:30:07Z","timestamp":1773217807056,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031343438","type":"print"},{"value":"9783031343445","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34344-5_46","type":"book-chapter","created":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T23:03:59Z","timestamp":1685919839000},"page":"373-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A General-Purpose AI Assistant Embedded in\u00a0an\u00a0Open-Source Radiology Information System"],"prefix":"10.1007","author":[{"given":"Saptarshi","family":"Purkayastha","sequence":"first","affiliation":[]},{"given":"Rohan","family":"Isaac","sequence":"additional","affiliation":[]},{"given":"Sharon","family":"Anthony","sequence":"additional","affiliation":[]},{"given":"Shikhar","family":"Shukla","sequence":"additional","affiliation":[]},{"given":"Elizabeth A.","family":"Krupinski","sequence":"additional","affiliation":[]},{"given":"Joshua A.","family":"Danish","sequence":"additional","affiliation":[]},{"given":"Judy Wawira","family":"Gichoya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"issue":"3","key":"46_CR1","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1148\/radiol.212151","volume":"305","author":"D Daye","year":"2022","unstructured":"Daye, D., et al.: Implementation of clinical artificial intelligence in radiology: who decides and how? Radiology 305(3), 555\u2013563 (2022)","journal-title":"Radiology"},{"issue":"1","key":"46_CR2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.acra.2019.09.014","volume":"27","author":"HM Do","year":"2020","unstructured":"Do, H.M., et al.: Augmented radiologist workflow improves report value and saves time: a potential model for implementation of artificial intelligence. Acad. Radiol. 27(1), 96\u2013105 (2020)","journal-title":"Acad. Radiol."},{"issue":"3","key":"46_CR3","doi-asserted-by":"publisher","first-page":"e106","DOI":"10.1016\/S2589-7500(20)30019-4","volume":"2","author":"M Dustler","year":"2020","unstructured":"Dustler, M.: Evaluating ai in breast cancer screening: a complex task. Lancet Digital Health 2(3), e106\u2013e107 (2020)","journal-title":"Lancet Digital Health"},{"issue":"3","key":"46_CR4","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10278-018-0088-5","volume":"31","author":"JW Gichoya","year":"2018","unstructured":"Gichoya, J.W., Kohli, M., Ivange, L., Schmidt, T.S., Purkayastha, S.: A platform for innovation and standards evaluation: a case study from the openmrs open-source radiology information system. J. Digit. Imaging 31(3), 361\u2013370 (2018)","journal-title":"J. Digit. Imaging"},{"issue":"3","key":"46_CR5","doi-asserted-by":"publisher","first-page":"e191095","DOI":"10.1001\/jamanetworkopen.2019.1095","volume":"2","author":"EJ Hwang","year":"2019","unstructured":"Hwang, E.J., et al.: Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw. Open 2(3), e191095\u2013e191095 (2019)","journal-title":"JAMA Netw. Open"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Linguraru, M.G., Maier-Hein, L., Summers, R.M., Kahn Jr, C.E.: RSNA-MICCAI panel discussion: 2. leveraging the full potential of ai-radiologists and data scientists working together. Radiology: Artif. Intell. 3(6) (2021)","DOI":"10.1148\/ryai.2021210248"},{"key":"46_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2021.04.008","volume":"76","author":"G Maguolo","year":"2021","unstructured":"Maguolo, G., Nanni, L.: A critic evaluation of methods for covid-19 automatic detection from x-ray images. Inf. Fusion 76, 1\u20137 (2021)","journal-title":"Inf. Fusion"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Mazaheri, S., Loya, M.F., Newsome, J., Lungren, M., Gichoya, J.W.: Challenges of implementing artificial intelligence in interventional radiology. In: Seminars in Interventional Radiology, vol. 38, pp. 554\u2013559. Thieme Medical Publishers, Inc. (2021)","DOI":"10.1055\/s-0041-1736659"},{"issue":"5","key":"46_CR9","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1111\/1754-9485.13273","volume":"65","author":"DA Moses","year":"2021","unstructured":"Moses, D.A.: Deep learning applied to automatic disease detection using chest x-rays. J. Med. Imaging Radiat. Oncol. 65(5), 498\u2013517 (2021)","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Omoumi, P., et al.: To buy or not to buy-evaluating commercial ai solutions in radiology (the eclair guidelines). Europ. Radiol. 31(6), 3786\u20133796 (2021)","DOI":"10.1007\/s00330-020-07684-x"},{"key":"46_CR11","unstructured":"Rajpurkar, P., et al.: Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)"},{"issue":"6","key":"46_CR12","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1038\/s41591-022-01768-5","volume":"28","author":"OL Saldanha","year":"2022","unstructured":"Saldanha, O.L., et al.: Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat. Med. 28(6), 1232\u20131239 (2022)","journal-title":"Nat. Med."},{"issue":"10","key":"46_CR13","doi-asserted-by":"publisher","first-page":"5525","DOI":"10.1007\/s00330-020-06946-y","volume":"30","author":"L Strohm","year":"2020","unstructured":"Strohm, L., Hehakaya, C., Ranschaert, E.R., Boon, W.P., Moors, E.H.: Implementation of artificial intelligence (ai) applications in radiology: hindering and facilitating factors. Eur. Radiol. 30(10), 5525\u20135532 (2020)","journal-title":"Eur. Radiol."},{"issue":"11","key":"46_CR14","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1016\/j.jacr.2020.08.018","volume":"17","author":"A Tariq","year":"2020","unstructured":"Tariq, A., et al.: Current clinical applications of artificial intelligence in radiology and their best supporting evidence. J. Am. Coll. Radiol. 17(11), 1371\u20131381 (2020)","journal-title":"J. Am. Coll. Radiol."},{"issue":"7862","key":"46_CR15","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-021-03583-3","volume":"594","author":"S Warnat-Herresthal","year":"2021","unstructured":"Warnat-Herresthal, S., et al.: Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862), 265\u2013270 (2021)","journal-title":"Nature"},{"issue":"9","key":"46_CR16","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1097\/RLI.0000000000000673","volume":"55","author":"JL Wichmann","year":"2020","unstructured":"Wichmann, J.L., Willemink, M.J., De Cecco, C.N.: Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation. Invest. Radiol. 55(9), 619\u2013627 (2020)","journal-title":"Invest. Radiol."},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Yang, L., Ene, I.C., Arabi Belaghi, R., Koff, D., Stein, N., Santaguida, P.: Stakeholders\u2019 perspectives on the future of artificial intelligence in radiology: a scoping review. Europ. Radiol. 32(3), 1477\u20131495 (2022)","DOI":"10.1007\/s00330-021-08214-z"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34344-5_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:44:52Z","timestamp":1709819092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34344-5_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031343438","9783031343445"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34344-5_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portoroz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aimedicine.info\/aime23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EASY CHAIR","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3 (+ 1 meta-review)","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3 (demonstration papers, similar to short papers)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}