{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:25:21Z","timestamp":1775067921824,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819638628","type":"print"},{"value":"9789819638635","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-3863-5_34","type":"book-chapter","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:39:29Z","timestamp":1743824369000},"page":"373-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Impact of\u00a0Deep Learning Aid on\u00a0the\u00a0Workload and\u00a0Interpretation Accuracy of\u00a0Radiologists on\u00a0Chest Computed Tomography"],"prefix":"10.1007","author":[{"given":"Anvar","family":"Kurmukov","sequence":"first","affiliation":[]},{"given":"Valeria","family":"Chernina","sequence":"additional","affiliation":[]},{"given":"Regina","family":"Gareeva","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Dugova","sequence":"additional","affiliation":[]},{"given":"Ekaterina","family":"Petrash","sequence":"additional","affiliation":[]},{"given":"Olga","family":"Aleshina","sequence":"additional","affiliation":[]},{"given":"Maxim","family":"Pisov","sequence":"additional","affiliation":[]},{"given":"Boris","family":"Shirokikh","sequence":"additional","affiliation":[]},{"given":"Valentin","family":"Samokhin","sequence":"additional","affiliation":[]},{"given":"Vladislav","family":"Proskurov","sequence":"additional","affiliation":[]},{"given":"Stanislav","family":"Shimovolos","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Basova","sequence":"additional","affiliation":[]},{"given":"Mikhail","family":"Goncharov","sequence":"additional","affiliation":[]},{"given":"Eugenia","family":"Soboleva","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Donskova","sequence":"additional","affiliation":[]},{"given":"Farukh","family":"Yaushev","sequence":"additional","affiliation":[]},{"given":"Alexey","family":"Shevtsov","sequence":"additional","affiliation":[]},{"given":"Alexey","family":"Zakharov","sequence":"additional","affiliation":[]},{"given":"Talgat","family":"Saparov","sequence":"additional","affiliation":[]},{"given":"Victor","family":"Gombolevskiy","sequence":"additional","affiliation":[]},{"given":"Mikhail","family":"Belyaev","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"issue":"3","key":"34_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1097\/RTI.0000000000000613","volume":"37","author":"AF Abadia","year":"2022","unstructured":"Abadia, A.F., et al.: Diagnostic accuracy and performance of artificial intelligence in detecting lung nodules in patients with complex lung disease: a noninferiority study. J. Thorac. Imaging 37(3), 154\u2013161 (2022)","journal-title":"J. Thorac. Imaging"},{"issue":"8","key":"34_CR2","doi-asserted-by":"publisher","first-page":"e2229289","DOI":"10.1001\/jamanetworkopen.2022.29289","volume":"5","author":"JS Ahn","year":"2022","unstructured":"Ahn, J.S., et al.: Association of artificial intelligence-aided chest radiograph interpretation with reader performance and efficiency. JAMA Netw. Open 5(8), e2229289\u2013e2229289 (2022)","journal-title":"JAMA Netw. Open"},{"key":"34_CR3","unstructured":"Association of American Medical Colleges: The complexities of physician supply and demand: Projections from 2019 to 2034 (2021). https:\/\/www.aamc.org\/media\/54681\/download?attachment"},{"issue":"3","key":"34_CR4","doi-asserted-by":"publisher","first-page":"e230860","DOI":"10.1148\/radiol.230860","volume":"309","author":"S Bennani","year":"2023","unstructured":"Bennani, S., et al.: Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiology 309(3), e230860 (2023)","journal-title":"Radiology"},{"key":"34_CR5","doi-asserted-by":"publisher","first-page":"102125","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K.G., Murphy, K.: Deep learning for chest x-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021)","journal-title":"Med. Image Anal."},{"issue":"3","key":"34_CR6","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1148\/radiol.2021204021","volume":"301","author":"DK Eng","year":"2021","unstructured":"Eng, D.K., et al.: Artificial intelligence algorithm improves radiologist performance in skeletal age assessment: a prospective multicenter randomized controlled trial. Radiology 301(3), 692\u2013699 (2021)","journal-title":"Radiology"},{"key":"34_CR7","unstructured":"European Society of Cardiology: Aortic diseases clinical practice guidelines. https:\/\/www.escardio.org\/Guidelines\/Clinical-Practice-Guidelines\/Aortic-Diseases"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"6808","DOI":"10.1007\/s00330-020-07033-y","volume":"30","author":"M Francone","year":"2020","unstructured":"Francone, M., et al.: Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur. Radiol. 30, 6808\u20136817 (2020)","journal-title":"Eur. Radiol."},{"issue":"930","key":"34_CR9","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1136\/pmj.79.930.214","volume":"79","author":"M Gatt","year":"2003","unstructured":"Gatt, M., Spectre, G., Paltiel, O., Hiller, N., Stalnikowicz, R.: Chest radiographs in the emergency department: is the radiologist really necessary? Postgrad. Med. J. 79(930), 214\u2013217 (2003)","journal-title":"Postgrad. Med. J."},{"issue":"9","key":"34_CR10","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1002\/jbmr.5650080915","volume":"8","author":"HK Genant","year":"1993","unstructured":"Genant, H.K., Wu, C.Y., Van Kuijk, C., Nevitt, M.C.: Vertebral fracture assessment using a semiquantitative technique. J. Bone Miner. Res. 8(9), 1137\u20131148 (1993)","journal-title":"J. Bone Miner. Res."},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1007\/s00261-019-02149-2","volume":"45","author":"DI Glazer","year":"2020","unstructured":"Glazer, D.I., Mayo-Smith, W.W.: Management of incidental adrenal masses: an update. Abdom. Radiol. 45, 892\u2013900 (2020)","journal-title":"Abdom. Radiol."},{"issue":"6","key":"34_CR12","doi-asserted-by":"publisher","first-page":"4223","DOI":"10.1007\/s00330-022-09381-3","volume":"33","author":"L Gorenstein","year":"2023","unstructured":"Gorenstein, L., Soffer, S., Apter, S., Konen, E., Klang, E.: AI in radiology: is it the time for randomized controlled trials? Eur. Radiol. 33(6), 4223\u20134225 (2023)","journal-title":"Eur. Radiol."},{"issue":"Suppl 8","key":"34_CR13","doi-asserted-by":"publisher","first-page":"S1078","DOI":"10.21037\/jtd.2019.04.109","volume":"11","author":"Z He","year":"2019","unstructured":"He, Z., et al.: The ideal methods for the management of rib fractures. J. Thorac. Dis. 11(Suppl 8), S1078 (2019)","journal-title":"J. Thorac. Dis."},{"key":"34_CR14","unstructured":"International Society for Clinical Densitometry: Official positions 2023 (2023). https:\/\/iscd.org\/official-positions-2023\/"},{"issue":"6","key":"34_CR15","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu, X., et al.: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1(6), e271\u2013e297 (2019)","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"34_CR16","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1148\/radiol.2017161659","volume":"284","author":"H MacMahon","year":"2017","unstructured":"MacMahon, H., et al.: Guidelines for management of incidental pulmonary nodules detected on ct images: from the fleischner society 2017. Radiology 284(1), 228\u2013243 (2017)","journal-title":"Radiology"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Munden, R.F., et\u00a0al.: Managing incidental findings on thoracic CT: mediastinal and cardiovascular findings. a white paper of the ACR incidental findings committee. J. Am. Coll. Radiol. 15(8), 1087\u20131096 (2018)","DOI":"10.1016\/j.jacr.2018.04.029"},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12931-019-1049-3","volume":"20","author":"M Occhipinti","year":"2019","unstructured":"Occhipinti, M., et al.: Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in copd. Respir. Res. 20, 1\u201311 (2019)","journal-title":"Respir. Res."},{"key":"34_CR19","unstructured":"Radiological Society of North America: Global radiologist shortage (2022). https:\/\/www.rsna.org\/news\/2022\/may\/global-radiologist-shortage"},{"issue":"1","key":"34_CR20","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.acra.2020.01.035","volume":"28","author":"B Rao","year":"2021","unstructured":"Rao, B., Zohrabian, V., Cedeno, P., Saha, A., Pahade, J., Davis, M.A.: Utility of artificial intelligence tool as a prospective radiology peer reviewer\u2013detection of unreported intracranial hemorrhage. Acad. Radiol. 28(1), 85\u201393 (2021)","journal-title":"Acad. Radiol."},{"issue":"2","key":"34_CR21","doi-asserted-by":"publisher","first-page":"133","DOI":"10.7861\/fhj.2022-0052","volume":"9","author":"M Richards","year":"2022","unstructured":"Richards, M., Maskell, G., Halliday, K., Allen, M.: Diagnostics: a major priority for the NHS. Fut. Healthc. J. 9(2), 133 (2022)","journal-title":"Fut. Healthc. J."},{"issue":"7","key":"34_CR22","doi-asserted-by":"publisher","first-page":"3037","DOI":"10.1109\/JBHI.2022.3153394","volume":"26","author":"B Shirokikh","year":"2022","unstructured":"Shirokikh, B., et al.: Systematic clinical evaluation of a deep learning method for medical image segmentation: radiosurgery application. IEEE J. Biomed. Health Inform. 26(7), 3037\u20133046 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1099","key":"34_CR23","doi-asserted-by":"publisher","first-page":"20190043","DOI":"10.1259\/bjr.20190043","volume":"92","author":"S Taylor-Phillips","year":"2019","unstructured":"Taylor-Phillips, S., Stinton, C.: Fatigue in radiology: a fertile area for future research. Br. J. Radiol. 92(1099), 20190043 (2019)","journal-title":"Br. J. Radiol."},{"key":"34_CR24","unstructured":"The Royal College of Radiologists: Clinical radiology census reports (2024). https:\/\/www.rcr.ac.uk\/media\/qs0jnfmv\/rcr-census_clinical-radiology-workforce-census_2022.pdf"},{"issue":"8","key":"34_CR25","doi-asserted-by":"publisher","first-page":"e525","DOI":"10.1016\/S2589-7500(23)00107-3","volume":"5","author":"D Ueda","year":"2023","unstructured":"Ueda, D., et al.: Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study. Lancet Digit. Health 5(8), e525\u2013e533 (2023)","journal-title":"Lancet Digit. Health"},{"key":"34_CR26","unstructured":"Vay\u00e1, M.D.L.I., et\u00a0al.: Bimcv covid-19+: a large annotated dataset of RX and CT images from COVID-19 patients. arXiv preprint: arXiv:2006.01174 (2020)"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Winder, M., Owczarek, A., Chudek, J., Pilch-Kowalczyk, J., Baron, J.: Are we overdoing it? Changes in diagnostic imaging workload during the years 2010\u20132020 including the impact of the SARS-COV-2 pandemic. healthcare (basel) (2021)","DOI":"10.3390\/healthcare9111557"},{"issue":"5","key":"34_CR28","doi-asserted-by":"publisher","first-page":"743","DOI":"10.2214\/AJR.22.27598","volume":"219","author":"B Yacoub","year":"2022","unstructured":"Yacoub, B., et al.: Impact of artificial intelligence assistance on chest CT interpretation times: a prospective randomized study. Am. J. Roentgenol. 219(5), 743\u2013751 (2022)","journal-title":"Am. J. Roentgenol."}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3863-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:19:29Z","timestamp":1757153969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3863-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819638628","9789819638635"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3863-5_34","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"value":"1876-1100","type":"print"},{"value":"1876-1119","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micad.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}