{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:01:14Z","timestamp":1790395274060,"version":"4.1.0"},"reference-count":19,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000871","name":"Mayo Clinic","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacr.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American College of Radiology"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jacr.2025.12.028","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:08:10Z","timestamp":1766794090000},"page":"965-972","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Factors Impacting the Performance of Deep Learning Detection of Pulmonary Emboli"],"prefix":"10.1016","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-4686","authenticated-orcid":false,"given":"Vera","family":"Sorin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2516-1751","authenticated-orcid":false,"given":"Panagiotis","family":"Korfiatis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve G.","family":"Langer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lewis D.","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2445-2357","authenticated-orcid":false,"given":"Alex K.","family":"Bratt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cole J.","family":"Cook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6524-6537","authenticated-orcid":false,"given":"Joe D.","family":"Sobek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Crystal L.","family":"Butler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Wald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradley J.","family":"Erickson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0707-1736","authenticated-orcid":false,"given":"Jeremy D.","family":"Collins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jacr.2025.12.028_bib2","doi-asserted-by":"crossref","DOI":"10.1148\/rg.230067","article-title":"Understanding and mitigating bias in imaging artificial intelligence","volume":"44","author":"Tejani","year":"2024","journal-title":"Radiographics"},{"key":"10.1016\/j.jacr.2025.12.028_bib3","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.241674","article-title":"Pitfalls and best practices in evaluation of AI algorithmic biases in radiology","volume":"315","author":"Yi","year":"2025","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2025.12.028_bib4","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.231693","article-title":"Coarse race and ethnicity labels mask granular underdiagnosis disparities in deep learning models for chest radiograph diagnosis","volume":"309","author":"Bachina","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2025.12.028_bib5","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","article-title":"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations","volume":"27","author":"Seyyed-Kalantari","year":"2021","journal-title":"Nat Med"},{"key":"10.1016\/j.jacr.2025.12.028_bib6","doi-asserted-by":"crossref","first-page":"12592","DOI":"10.1073\/pnas.1919012117","article-title":"Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis","volume":"117","author":"Larrazabal","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"10.1016\/j.jacr.2025.12.028_bib7","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.232286","article-title":"Patient characteristics impact performance of AI algorithm in interpreting negative screening digital breast tomosynthesis studies","volume":"311","author":"Nguyen","year":"2024","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2025.12.028_bib8","article-title":"The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study","author":"Guo","year":"2024","journal-title":"arXiv preprint arXiv:240904368"},{"key":"10.1016\/j.jacr.2025.12.028_bib10","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/s41746-025-01482-9","article-title":"Could transparent model cards with layered accessible information drive trust and safety in health AI?","volume":"8","author":"Gilbert","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jacr.2025.12.028_bib11","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.clinimag.2022.01.016","article-title":"The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography","volume":"84","author":"Jonas","year":"2022","journal-title":"Clin Imaging"},{"key":"10.1016\/j.jacr.2025.12.028_bib12","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1016\/j.acra.2021.10.024","article-title":"Complex relationship between artificial intelligence and CT radiation dose","volume":"29","author":"Gupta","year":"2022","journal-title":"Acad Radiol"},{"key":"10.1016\/j.jacr.2025.12.028_bib13","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1016\/j.jacr.2025.06.036","article-title":"Using a large language model for post-deployment monitoring of FDA approved AI: pulmonary embolism detection use case","volume":"22","author":"Sorin","year":"2025","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.jacr.2025.12.028_bib15","article-title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","author":"Team","year":"2024","journal-title":"arXiv preprint arXiv:240305530"},{"key":"10.1016\/j.jacr.2025.12.028_bib16","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/j.cardfail.2023.07.006","article-title":"Heart failure epidemiology and outcomes statistics: a report of the Heart Failure Society of America","volume":"29","author":"Bozkurt","year":"2023","journal-title":"J Card Fail"},{"key":"10.1016\/j.jacr.2025.12.028_bib17","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.7150\/jca.26008","article-title":"Lung cancer and pulmonary embolism: what is the relationship? A review","volume":"9","author":"Li","year":"2018","journal-title":"J Cancer"},{"key":"10.1016\/j.jacr.2025.12.028_bib18","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2024.60315","article-title":"Pulmonary embolism\u2013related mortality in patients with cancer","volume":"8","author":"Zuin","year":"2025","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.jacr.2025.12.028_bib19","article-title":"Automated deep learning analysis for quality improvement of CT pulmonary angiography","volume":"4","author":"Hahn","year":"2022","journal-title":"Radiol Artif Intell"},{"issue":"3 Pt A","key":"10.1016\/j.jacr.2025.12.028_bib21","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.jacr.2020.09.060","article-title":"Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations","volume":"18","author":"Larson","year":"2021","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.jacr.2025.12.028_bib22","doi-asserted-by":"crossref","DOI":"10.2196\/42940","article-title":"Artificial intelligence algorithms in health care: is the current Food and Drug Administration regulation sufficient?","volume":"2","author":"Mashar","year":"2023","journal-title":"JMIR AI"},{"key":"10.1016\/j.jacr.2025.12.028_bib23","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41746-024-01270-x","article-title":"A scoping review of reporting gaps in FDA-approved AI medical devices","volume":"7","author":"Muralidharan","year":"2024","journal-title":"NPJ Digit Med"}],"container-title":["Journal of the American College of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S154614402500746X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S154614402500746X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:34:50Z","timestamp":1790393690000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S154614402500746X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":19,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S154614402500746X"],"URL":"https:\/\/doi.org\/10.1016\/j.jacr.2025.12.028","relation":{},"ISSN":["1546-1440"],"issn-type":[{"value":"1546-1440","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Factors Impacting the Performance of Deep Learning Detection of Pulmonary Emboli","name":"articletitle","label":"Article Title"},{"value":"Journal of the American College of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacr.2025.12.028","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 American College of Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}