{"id":322894,"date":"2026-03-05T09:03:11","date_gmt":"2026-03-05T09:03:11","guid":{"rendered":"https:\/\/www.newsbeep.com\/il\/322894\/"},"modified":"2026-03-05T09:03:11","modified_gmt":"2026-03-05T09:03:11","slug":"merlin-a-computed-tomography-vision-language-foundation-model-and-dataset","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/il\/322894\/","title":{"rendered":"Merlin: a computed tomography vision\u2013language foundation model and dataset"},"content":{"rendered":"<p class=\"c-article-references__text\" id=\"ref-CR1\">Sch\u00f6ckel, L. et al. Developments in X-ray contrast media and the potential impact on computed tomography. Invest. Radiol. 55, 592\u2013597 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1097\/RLI.0000000000000696\" data-track-item_id=\"10.1097\/RLI.0000000000000696\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1097%2FRLI.0000000000000696\" aria-label=\"Article reference 1\" data-doi=\"10.1097\/RLI.0000000000000696\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32701620\" aria-label=\"PubMed reference 1\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 1\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Developments%20in%20X-ray%20contrast%20media%20and%20the%20potential%20impact%20on%20computed%20tomography&amp;journal=Invest.%20Radiol.&amp;doi=10.1097%2FRLI.0000000000000696&amp;volume=55&amp;pages=592-597&amp;publication_year=2020&amp;author=Sch%C3%B6ckel%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Kanal, K. M. et al. U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 284, 120\u2013133 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/radiol.2017161911\" data-track-item_id=\"10.1148\/radiol.2017161911\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fradiol.2017161911\" aria-label=\"Article reference 2\" data-doi=\"10.1148\/radiol.2017161911\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=28221093\" aria-label=\"PubMed reference 2\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 2\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=U.S.%20diagnostic%20reference%20levels%20and%20achievable%20doses%20for%2010%20adult%20CT%20examinations&amp;journal=Radiology&amp;doi=10.1148%2Fradiol.2017161911&amp;volume=284&amp;pages=120-133&amp;publication_year=2017&amp;author=Kanal%2CKM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Taschetta-Millane, M. The evolving computed tomography market. Imaging Technology News <a href=\"https:\/\/www.itnonline.com\/article\/evolving-computed-tomography-market\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.itnonline.com\/article\/evolving-computed-tomography-market\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.itnonline.com\/article\/evolving-computed-tomography-market<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Hudnall, C. Maximum capacity: overloaded radiologists are grappling with solutions to a booming volume crisis. American College of Radiology <a href=\"https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/April-2024\/Maximum-Capacity\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/April-2024\/Maximum-Capacity\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/April-2024\/Maximum-Capacity<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">Milburn, J. Workforce-shortage. How will we solve our radiology workforce shortage? American College of Radiology <a href=\"https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/March-2024\/How-Will-We-Solve-Our-Radiology-Workforce-Shortage\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/March-2024\/How-Will-We-Solve-Our-Radiology-Workforce-Shortage\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/March-2024\/How-Will-We-Solve-Our-Radiology-Workforce-Shortage<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Rimmer, A. Radiologist shortage leaves patient care at risk, warns royal college. BMJ 359, j4683 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1136\/bmj.j4683\" data-track-item_id=\"10.1136\/bmj.j4683\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1136%2Fbmj.j4683\" aria-label=\"Article reference 6\" data-doi=\"10.1136\/bmj.j4683\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=29021184\" aria-label=\"PubMed reference 6\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 6\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Radiologist%20shortage%20leaves%20patient%20care%20at%20risk%2C%20warns%20royal%20college&amp;journal=BMJ&amp;doi=10.1136%2Fbmj.j4683&amp;volume=359&amp;publication_year=2017&amp;author=Rimmer%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">Paschali, M. et al. Foundation models in radiology: what, how, why, and why not. Radiology 314, e240597 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/radiol.240597\" data-track-item_id=\"10.1148\/radiol.240597\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fradiol.240597\" aria-label=\"Article reference 7\" data-doi=\"10.1148\/radiol.240597\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=39903075\" aria-label=\"PubMed reference 7\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11868850\" aria-label=\"PubMed Central reference 7\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 7\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Foundation%20models%20in%20radiology%3A%20what%2C%20how%2C%20why%2C%20and%20why%20not&amp;journal=Radiology&amp;doi=10.1148%2Fradiol.240597&amp;volume=314&amp;publication_year=2025&amp;author=Paschali%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Zhang, S. et al. A multimodal biomedical foundation model trained from fifteen million image\u2013text pairs. NEJM AI 2, AIoa2400640 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1056\/AIoa2400640\" data-track-item_id=\"10.1056\/AIoa2400640\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1056%2FAIoa2400640\" aria-label=\"Article reference 8\" data-doi=\"10.1056\/AIoa2400640\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 8\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20multimodal%20biomedical%20foundation%20model%20trained%20from%20fifteen%20million%20image%E2%80%93text%20pairs&amp;journal=NEJM%20AI&amp;doi=10.1056%2FAIoa2400640&amp;volume=2&amp;publication_year=2025&amp;author=Zhang%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Chaves, J. M. et al. A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings. Nat. Commun. 16, 3108 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-025-58344-x\" data-track-item_id=\"10.1038\/s41467-025-58344-x\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-025-58344-x\" aria-label=\"Article reference 9\" data-doi=\"10.1038\/s41467-025-58344-x\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2025NatCo..16.3108Z\" aria-label=\"ADS reference 9\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 9\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20clinically%20accessible%20small%20multimodal%20radiology%20model%20and%20evaluation%20metric%20for%20chest%20X-ray%20findings&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-025-58344-x&amp;volume=16&amp;publication_year=2025&amp;author=Chaves%2CJM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Tu, T. et al. Towards generalist biomedical AI. NEJM AI 1, AIoa2300138 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1056\/AIoa2300138\" data-track-item_id=\"10.1056\/AIoa2300138\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1056%2FAIoa2300138\" aria-label=\"Article reference 10\" data-doi=\"10.1056\/AIoa2300138\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 10\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Towards%20generalist%20biomedical%20AI&amp;journal=NEJM%20AI&amp;doi=10.1056%2FAIoa2300138&amp;volume=1&amp;publication_year=2024&amp;author=Tu%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Wu, C., Zhang, X., Zhang, Y., Wang, Y. &amp; Xie, W. Towards generalist foundation model for radiology by leveraging web-scale 2D &amp; 3D medical data. Nat. Commun. 16, 7866 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-025-62385-7\" data-track-item_id=\"10.1038\/s41467-025-62385-7\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-025-62385-7\" aria-label=\"Article reference 11\" data-doi=\"10.1038\/s41467-025-62385-7\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2025NatCo..16.7866W\" aria-label=\"ADS reference 11\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXitFCqtbfP\" aria-label=\"CAS reference 11\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=40849424\" aria-label=\"PubMed reference 11\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC12375113\" aria-label=\"PubMed Central reference 11\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 11\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Towards%20generalist%20foundation%20model%20for%20radiology%20by%20leveraging%20web-scale%202D%20%26%203D%20medical%20data&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-025-62385-7&amp;volume=16&amp;publication_year=2025&amp;author=Wu%2CC&amp;author=Zhang%2CX&amp;author=Zhang%2CY&amp;author=Wang%2CY&amp;author=Xie%2CW\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Chen, Z. et al. CheXagent: Towards a foundation model for chest X-ray interpretation. In AAAI 2024 Spring Symposium on Clinical Foundation Models (AAAI, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Udare, A. et al. Radiologist productivity analytics: factors impacting abdominal pelvic CT exam reporting times. J. Digit. Imaging 35, 87\u201397 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s10278-021-00548-w\" data-track-item_id=\"10.1007\/s10278-021-00548-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s10278-021-00548-w\" aria-label=\"Article reference 13\" data-doi=\"10.1007\/s10278-021-00548-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35013824\" aria-label=\"PubMed reference 13\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8921423\" aria-label=\"PubMed Central reference 13\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 13\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Radiologist%20productivity%20analytics%3A%20factors%20impacting%20abdominal%20pelvic%20CT%20exam%20reporting%20times&amp;journal=J.%20Digit.%20Imaging&amp;doi=10.1007%2Fs10278-021-00548-w&amp;volume=35&amp;pages=87-97&amp;publication_year=2022&amp;author=Udare%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Liu, D. et al. Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction. Abdom. Radiol. 48, 787\u2013795 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s00261-022-03728-6\" data-track-item_id=\"10.1007\/s00261-022-03728-6\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s00261-022-03728-6\" aria-label=\"Article reference 14\" data-doi=\"10.1007\/s00261-022-03728-6\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXlt1OksbY%3D\" aria-label=\"CAS reference 14\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 14\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fully%20automated%20CT-based%20adiposity%20assessment%3A%20comparison%20of%20the%20L1%20and%20L3%20vertebral%20levels%20for%20opportunistic%20prediction&amp;journal=Abdom.%20Radiol.&amp;doi=10.1007%2Fs00261-022-03728-6&amp;volume=48&amp;pages=787-795&amp;publication_year=2023&amp;author=Liu%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Blankemeier, L. et al. Opportunistic incidence prediction of multiple chronic diseases from abdominal CT imaging using multi-task learning. In Proc. 25th International Conference on Medical Image Computing and Computer-Assisted Intervention 309\u2013318 (Springer, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Zambrano Chaves, J. M. et al. Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach. Sci. Rep. 13, 21034 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41598-023-47895-y\" data-track-item_id=\"10.1038\/s41598-023-47895-y\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41598-023-47895-y\" aria-label=\"Article reference 16\" data-doi=\"10.1038\/s41598-023-47895-y\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NatSR..1321034Z\" aria-label=\"ADS reference 16\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXisFSmtb3E\" aria-label=\"CAS reference 16\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38030716\" aria-label=\"PubMed reference 16\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10687235\" aria-label=\"PubMed Central reference 16\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 16\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Opportunistic%20assessment%20of%20ischemic%20heart%20disease%20risk%20using%20abdominopelvic%20computed%20tomography%20and%20medical%20record%20data%3A%20a%20multimodal%20explainable%20artificial%20intelligence%20approach&amp;journal=Sci.%20Rep.&amp;doi=10.1038%2Fs41598-023-47895-y&amp;volume=13&amp;publication_year=2023&amp;author=Zambrano%20Chaves%2CJM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Cao, K. et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat. Med. 29, 3033\u20133043 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-023-02640-w\" data-track-item_id=\"10.1038\/s41591-023-02640-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-023-02640-w\" aria-label=\"Article reference 17\" data-doi=\"10.1038\/s41591-023-02640-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXisVektLjI\" aria-label=\"CAS reference 17\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37985692\" aria-label=\"PubMed reference 17\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10719100\" aria-label=\"PubMed Central reference 17\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 17\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20pancreatic%20cancer%20detection%20via%20non-contrast%20CT%20and%20deep%20learning&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-023-02640-w&amp;volume=29&amp;pages=3033-3043&amp;publication_year=2023&amp;author=Cao%2CK\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Wang, Y.-R. et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat. Med. 30, 1471\u20131480 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-024-02971-2\" data-track-item_id=\"10.1038\/s41591-024-02971-2\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-024-02971-2\" aria-label=\"Article reference 18\" data-doi=\"10.1038\/s41591-024-02971-2\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhtVGrs7bE\" aria-label=\"CAS reference 18\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38740996\" aria-label=\"PubMed reference 18\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11108784\" aria-label=\"PubMed Central reference 18\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 18\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Screening%20and%20diagnosis%20of%20cardiovascular%20disease%20using%20artificial%20intelligence-enabled%20cardiac%20magnetic%20resonance%20imaging&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-024-02971-2&amp;volume=30&amp;pages=1471-1480&amp;publication_year=2024&amp;author=Wang%2CY-R\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Langlotz, C. P. The future of AI and informatics in radiology: 10 predictions. Radiology 309, e231114 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/radiol.231114\" data-track-item_id=\"10.1148\/radiol.231114\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fradiol.231114\" aria-label=\"Article reference 19\" data-doi=\"10.1148\/radiol.231114\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37874234\" aria-label=\"PubMed reference 19\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10623186\" aria-label=\"PubMed Central reference 19\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 19\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20future%20of%20AI%20and%20informatics%20in%20radiology%3A%2010%20predictions&amp;journal=Radiology&amp;doi=10.1148%2Fradiol.231114&amp;volume=309&amp;publication_year=2023&amp;author=Langlotz%2CCP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices (US Food and Drug Administration, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning 8748\u20138763 (PMLR, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Schuhmann, C. et al. Laion-5b: an open large-scale dataset for training next generation image-text models. Adv. Neural Inf. Process. Syst. 35, 25278\u201325294 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 22\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Laion-5b%3A%20an%20open%20large-scale%20dataset%20for%20training%20next%20generation%20image-text%20models&amp;journal=Adv.%20Neural%20Inf.%20Process.%20Syst.&amp;volume=35&amp;pages=25278-25294&amp;publication_year=2022&amp;author=Schuhmann%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H. &amp; Langlotz, C. P. Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology 295, 675\u2013682 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/radiol.2020192536\" data-track-item_id=\"10.1148\/radiol.2020192536\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fradiol.2020192536\" aria-label=\"Article reference 23\" data-doi=\"10.1148\/radiol.2020192536\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32208097\" aria-label=\"PubMed reference 23\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 23\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Ethics%20of%20using%20and%20sharing%20clinical%20imaging%20data%20for%20artificial%20intelligence%3A%20a%20proposed%20framework&amp;journal=Radiology&amp;doi=10.1148%2Fradiol.2020192536&amp;volume=295&amp;pages=675-682&amp;publication_year=2020&amp;author=Larson%2CDB&amp;author=Magnus%2CDC&amp;author=Lungren%2CMP&amp;author=Shah%2CNH&amp;author=Langlotz%2CCP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Hyland, S. L. et al. MAIRA-1: a specialised large multimodal model for radiology report generation. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2311.13668\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2311.13668\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2311.13668<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Huang, S.-C. et al. PENet\u2014a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. npj Digit. Med. 3, 61 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41746-020-0266-y\" data-track-item_id=\"10.1038\/s41746-020-0266-y\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41746-020-0266-y\" aria-label=\"Article reference 25\" data-doi=\"10.1038\/s41746-020-0266-y\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32352039\" aria-label=\"PubMed reference 25\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7181770\" aria-label=\"PubMed Central reference 25\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 25\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=PENet%E2%80%94a%20scalable%20deep-learning%20model%20for%20automated%20diagnosis%20of%20pulmonary%20embolism%20using%20volumetric%20CT%20imaging&amp;journal=npj%20Digit.%20Med.&amp;doi=10.1038%2Fs41746-020-0266-y&amp;volume=3&amp;publication_year=2020&amp;author=Huang%2CS-C\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Christensen, M., Vukadinovic, M., Yuan, N. &amp; Ouyang, D. Vision\u2013language foundation model for echocardiogram interpretation. Nat. Med. 30, 1481\u20131488 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-024-02959-y\" data-track-item_id=\"10.1038\/s41591-024-02959-y\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-024-02959-y\" aria-label=\"Article reference 26\" data-doi=\"10.1038\/s41591-024-02959-y\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXpsFGrt7o%3D\" aria-label=\"CAS reference 26\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38689062\" aria-label=\"PubMed reference 26\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11108770\" aria-label=\"PubMed Central reference 26\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 26\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Vision%E2%80%93language%20foundation%20model%20for%20echocardiogram%20interpretation&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-024-02959-y&amp;volume=30&amp;pages=1481-1488&amp;publication_year=2024&amp;author=Christensen%2CM&amp;author=Vukadinovic%2CM&amp;author=Yuan%2CN&amp;author=Ouyang%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Polevikov, S. Med-gemini by Google: A boon for researchers, a bane for doctors. AI Health Uncut <a href=\"https:\/\/sergeiai.substack.com\/p\/googles-med-gemini-im-excited-and\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/sergeiai.substack.com\/p\/googles-med-gemini-im-excited-and\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/sergeiai.substack.com\/p\/googles-med-gemini-im-excited-and<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">Fleming, S. L. et al. Medalign: a clinician-generated dataset for instruction following with electronic medical records. Proc. AAAI Conf. Artif. Intell. 38, 22021\u201322030 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=41584261\" aria-label=\"PubMed reference 28\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC12826664\" aria-label=\"PubMed Central reference 28\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 28\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Medalign%3A%20a%20clinician-generated%20dataset%20for%20instruction%20following%20with%20electronic%20medical%20records&amp;journal=Proc.%20AAAI%20Conf.%20Artif.%20Intell.&amp;volume=38&amp;pages=22021-22030&amp;publication_year=2024&amp;author=Fleming%2CSL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">Liebl, H. et al. A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data. Sci. Data 8, 284 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41597-021-01060-0\" data-track-item_id=\"10.1038\/s41597-021-01060-0\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41597-021-01060-0\" aria-label=\"Article reference 29\" data-doi=\"10.1038\/s41597-021-01060-0\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34711848\" aria-label=\"PubMed reference 29\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8553749\" aria-label=\"PubMed Central reference 29\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 29\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20computed%20tomography%20vertebral%20segmentation%20dataset%20with%20anatomical%20variations%20and%20multi-vendor%20scanner%20data&amp;journal=Sci.%20Data&amp;doi=10.1038%2Fs41597-021-01060-0&amp;volume=8&amp;publication_year=2021&amp;author=Liebl%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">Wasserthal, J. et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5, e230024 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/ryai.230024\" data-track-item_id=\"10.1148\/ryai.230024\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fryai.230024\" aria-label=\"Article reference 30\" data-doi=\"10.1148\/ryai.230024\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37795137\" aria-label=\"PubMed reference 30\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10546353\" aria-label=\"PubMed Central reference 30\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 30\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=TotalSegmentator%3A%20robust%20segmentation%20of%20104%20anatomic%20structures%20in%20CT%20images&amp;journal=Radiol.%20Artif.%20Intell.&amp;doi=10.1148%2Fryai.230024&amp;volume=5&amp;publication_year=2023&amp;author=Wasserthal%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Cherti, M. et al. Reproducible scaling laws for contrastive language\u2013image learning. In Proc. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2818\u20132829 (IEEE, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">L\u00f6ffler, M. T. et al. A vertebral segmentation dataset with fracture grading. Radiol. Artif. Intell. 2, e190138 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/ryai.2020190138\" data-track-item_id=\"10.1148\/ryai.2020190138\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fryai.2020190138\" aria-label=\"Article reference 32\" data-doi=\"10.1148\/ryai.2020190138\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33937831\" aria-label=\"PubMed reference 32\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8082364\" aria-label=\"PubMed Central reference 32\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 32\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20vertebral%20segmentation%20dataset%20with%20fracture%20grading&amp;journal=Radiol.%20Artif.%20Intell.&amp;doi=10.1148%2Fryai.2020190138&amp;volume=2&amp;publication_year=2020&amp;author=L%C3%B6ffler%2CMT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR33\">Carreira, J. &amp; Zisserman, A. Quo vadis, action recognition? A new model and the kinetics dataset. In Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition 6299\u20136308 (IEEE, 2017).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">Denny, J. C. et al. Systematic comparison of phenomewide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102\u20131111 (2013).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nbt.2749\" data-track-item_id=\"10.1038\/nbt.2749\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnbt.2749\" aria-label=\"Article reference 34\" data-doi=\"10.1038\/nbt.2749\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3sXhvVaiu7jE\" aria-label=\"CAS reference 34\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=24270849\" aria-label=\"PubMed reference 34\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3969265\" aria-label=\"PubMed Central reference 34\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 34\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Systematic%20comparison%20of%20phenomewide%20association%20study%20of%20electronic%20medical%20record%20data%20and%20genome-wide%20association%20study%20data&amp;journal=Nat.%20Biotechnol.&amp;doi=10.1038%2Fnbt.2749&amp;volume=31&amp;pages=1102-1111&amp;publication_year=2013&amp;author=Denny%2CJC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">Liu, Z. et al. A convnet for the 2020s. In Proc. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 11976\u201311986 (IEEE, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. In Proc. 2021 IEEE\/CVF International Conference on Computer Vision 10012\u201310022 (IEEE, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Li, Y., Wehbe, R. M., Ahmad, F. S., Wang, H. &amp; Luo, Y. Clinical-Longformer and Clinical-BigBird: transformers for long clinical sequences. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2201.11838\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2201.11838\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2201.11838<\/a> (2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Delbrouck, J.-B. et al. Improving the factual correctness of radiology report generation with semantic rewards. In Findings of the Association for Computational Linguistics: EMNLP 2022 4348\u20134360 (Association for Computational Linguistics, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q. &amp; Artzi, Y. BERTScore: Evaluating text generation with BERT. In International Conference on Learning Representations (ICLR, 2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Lin, C.-Y. ROUGE: a package for automatic evaluation of summaries. In Proc. Text Summarization Branches Out 74\u201381 (Association for Computational Linguistics, 2004).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Papineni, K., Roukos, S., Ward, T. &amp; Zhu, W.-J. BLEU: a method for automatic evaluation of machine translation. In Proc. 40th Annual Meeting of the Association for Computational Linguistics 311\u2013318 (Association of Computational Linguistics, 2002).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR42\">Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J. &amp; Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41592-020-01008-z\" data-track-item_id=\"10.1038\/s41592-020-01008-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41592-020-01008-z\" aria-label=\"Article reference 42\" data-doi=\"10.1038\/s41592-020-01008-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXisFSltL%2FN\" aria-label=\"CAS reference 42\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33288961\" aria-label=\"PubMed reference 42\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 42\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=nnU-Net%3A%20a%20self-configuring%20method%20for%20deep%20learning-based%20biomedical%20image%20segmentation&amp;journal=Nat.%20Methods&amp;doi=10.1038%2Fs41592-020-01008-z&amp;volume=18&amp;pages=203-211&amp;publication_year=2021&amp;author=Isensee%2CF&amp;author=Jaeger%2CPF&amp;author=Kohl%2CSAA&amp;author=Petersen%2CJ&amp;author=Maier-Hein%2CKH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR43\">Codella, N. C. F. et al. MedImageInsight: an open-source embedding model for general domain medical imaging. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2410.06542\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2410.06542\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2410.06542<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR44\">Yang, L. et al. Advancing multimodal medical capabilities of Gemini. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2405.03162\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2405.03162\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2405.03162<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR45\">Hamamci, I. E. et al. Developing generalist foundation models from a multimodal dataset for 3D computed tomography. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2403.17834\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2403.17834\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2403.17834<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR46\">Niu, C. et al. Medical multimodal multitask foundation model for lung cancer screening. Nat. Commun. 16, 1523 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-025-56822-w\" data-track-item_id=\"10.1038\/s41467-025-56822-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-025-56822-w\" aria-label=\"Article reference 46\" data-doi=\"10.1038\/s41467-025-56822-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2025NatCo..16.1523N\" aria-label=\"ADS reference 46\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXjvVGmsbw%3D\" aria-label=\"CAS reference 46\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=39934138\" aria-label=\"PubMed reference 46\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11814333\" aria-label=\"PubMed Central reference 46\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 46\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Medical%20multimodal%20multitask%20foundation%20model%20for%20lung%20cancer%20screening&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-025-56822-w&amp;volume=16&amp;publication_year=2025&amp;author=Niu%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR47\">Pai, S. et al. Vision foundation models for computed tomography. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2501.09001\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2501.09001\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2501.09001<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR48\">Huang, S.-C. et al. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. npj Digit. Med. 6, 74 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41746-023-00811-0\" data-track-item_id=\"10.1038\/s41746-023-00811-0\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41746-023-00811-0\" aria-label=\"Article reference 48\" data-doi=\"10.1038\/s41746-023-00811-0\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37100953\" aria-label=\"PubMed reference 48\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10131505\" aria-label=\"PubMed Central reference 48\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 48\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Self-supervised%20learning%20for%20medical%20image%20classification%3A%20a%20systematic%20review%20and%20implementation%20guidelines&amp;journal=npj%20Digit.%20Med.&amp;doi=10.1038%2Fs41746-023-00811-0&amp;volume=6&amp;publication_year=2023&amp;author=Huang%2CS-C\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR49\">Tang, Y. et al. Self-supervised pre-training of Swin transformers for 3D medical image analysis. In Proc. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 20730\u201320740 (IEEE, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR50\">He, K. et al. Masked autoencoders are scalable vision learners. In Proc. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 16000\u201316009 (IEEE, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR51\">Lauren\u00e7on, H., Tronchon, L., Cord, M. &amp; Sanh, V. What matters when building vision-language models? In Proc. 38th International Conference on Neural Information Processing Systems 87874\u201387907 (NIPS, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR52\">Li, Z. et al. Monkey: Image resolution and text label are important things for large multi-modal models. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 26763\u201326773 (IEEE, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR53\">Chen, T., Kornblith, S., Norouzi, M. &amp; Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. International Conference on Machine Learning 1597\u20131607 (PMLR, 2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR54\">Van den Oord, A., Li, Y. &amp; Vinyals, O. Representation learning with contrastive predictive coding. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1807.03748\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/1807.03748\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/1807.03748<\/a> (2018).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR55\">Reis, E. P. Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm. Eur. Radiol. 34, 6680\u20136687 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s00330-024-10769-6\" data-track-item_id=\"10.1007\/s00330-024-10769-6\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s00330-024-10769-6\" aria-label=\"Article reference 55\" data-doi=\"10.1007\/s00330-024-10769-6\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38683384\" aria-label=\"PubMed reference 55\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11456344\" aria-label=\"PubMed Central reference 55\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 55\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Automated%20abdominal%20CT%20contrast%20phase%20detection%20using%20an%20interpretable%20and%20open-source%20artificial%20intelligence%20algorithm&amp;journal=Eur.%20Radiol.&amp;doi=10.1007%2Fs00330-024-10769-6&amp;volume=34&amp;pages=6680-6687&amp;publication_year=2024&amp;author=Reis%2CEP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR56\">Van Uden, C. et al. Exploring the versatility of zero-shot CLIP for interstitial lung disease classification. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2306.01111\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2306.01111\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2306.01111<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR57\">Loshchilov, I. &amp; Hutter, F. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR, 2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR58\">Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521\u20133526 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1073\/pnas.1611835114\" data-track-item_id=\"10.1073\/pnas.1611835114\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1073%2Fpnas.1611835114\" aria-label=\"Article reference 58\" data-doi=\"10.1073\/pnas.1611835114\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2017PNAS..114.3521K\" aria-label=\"ADS reference 58\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=3635506\" aria-label=\"MathSciNet reference 58\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2sXktFylsbc%3D\" aria-label=\"CAS reference 58\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=28292907\" aria-label=\"PubMed reference 58\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5380101\" aria-label=\"PubMed Central reference 58\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 58\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Overcoming%20catastrophic%20forgetting%20in%20neural%20networks&amp;journal=Proc.%20Natl%20Acad.%20Sci.%20USA&amp;doi=10.1073%2Fpnas.1611835114&amp;volume=114&amp;pages=3521-3526&amp;publication_year=2017&amp;author=Kirkpatrick%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR59\">Chronic Kidney Disease in the United States, 2023 (Centers for Disease Control and Prevention, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR60\">By the Numbers: Diabetes in America (Centers for Disease Control and Prevention, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR61\">Facts about Hypertension (Centers for Disease Control and Prevention, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR62\">What is Coronary Heart Disease? (US Department of Health and Human Services, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR63\">Gu, J., Sanchez, R., Chauhan, A., Fazio, S. &amp; Wong, N. Lipid treatment status and goal attainment among patients with atherosclerotic cardiovascular disease in the United States: a 2019 update. Am. J. Prev. Cardiol. 10, 100336 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.ajpc.2022.100336\" data-track-item_id=\"10.1016\/j.ajpc.2022.100336\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.ajpc.2022.100336\" aria-label=\"Article reference 63\" data-doi=\"10.1016\/j.ajpc.2022.100336\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35368909\" aria-label=\"PubMed reference 63\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8968014\" aria-label=\"PubMed Central reference 63\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 63\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Lipid%20treatment%20status%20and%20goal%20attainment%20among%20patients%20with%20atherosclerotic%20cardiovascular%20disease%20in%20the%20United%20States%3A%20a%202019%20update&amp;journal=Am.%20J.%20Prev.%20Cardiol.&amp;doi=10.1016%2Fj.ajpc.2022.100336&amp;volume=10&amp;publication_year=2022&amp;author=Gu%2CJ&amp;author=Sanchez%2CR&amp;author=Chauhan%2CA&amp;author=Fazio%2CS&amp;author=Wong%2CN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR64\">Wright, N. C. et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J. Bone Miner. Res. 29, 2520\u20132526 (2014).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1002\/jbmr.2269\" data-track-item_id=\"10.1002\/jbmr.2269\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1002%2Fjbmr.2269\" aria-label=\"Article reference 64\" data-doi=\"10.1002\/jbmr.2269\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=24771492\" aria-label=\"PubMed reference 64\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4757905\" aria-label=\"PubMed Central reference 64\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 64\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20recent%20prevalence%20of%20osteoporosis%20and%20low%20bone%20mass%20in%20the%20United%20States%20based%20on%20bone%20mineral%20density%20at%20the%20femoral%20neck%20or%20lumbar%20spine&amp;journal=J.%20Bone%20Miner.%20Res.&amp;doi=10.1002%2Fjbmr.2269&amp;volume=29&amp;pages=2520-2526&amp;publication_year=2014&amp;author=Wright%2CNC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR65\">Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41597-022-01899-x\" data-track-item_id=\"10.1038\/s41597-022-01899-x\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41597-022-01899-x\" aria-label=\"Article reference 65\" data-doi=\"10.1038\/s41597-022-01899-x\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXit1Sit7fI\" aria-label=\"CAS reference 65\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36596836\" aria-label=\"PubMed reference 65\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9810617\" aria-label=\"PubMed Central reference 65\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 65\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=MIMIC-IV%2C%20a%20freely%20accessible%20electronic%20health%20record%20dataset&amp;journal=Sci.%20Data&amp;doi=10.1038%2Fs41597-022-01899-x&amp;volume=10&amp;publication_year=2023&amp;author=Johnson%2CAEW\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR66\">Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR67\">Van Veen, D. et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat. Med. 30, 1134\u20131142 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-024-02855-5\" data-track-item_id=\"10.1038\/s41591-024-02855-5\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-024-02855-5\" aria-label=\"Article reference 67\" data-doi=\"10.1038\/s41591-024-02855-5\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38413730\" aria-label=\"PubMed reference 67\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11479659\" aria-label=\"PubMed Central reference 67\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 67\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Adapted%20large%20language%20models%20can%20outperform%20medical%20experts%20in%20clinical%20text%20summarization&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-024-02855-5&amp;volume=30&amp;pages=1134-1142&amp;publication_year=2024&amp;author=Veen%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR68\">Van Veen, D. et al. RadAdapt: radiology report summarization via lightweight domain adaptation of large language models. In Proc. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks 449\u2013460 (Association for Computational Linguistics, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR69\">Ronneberger, O., Fischer, P. &amp; Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Proc. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015 234\u2013241 (Springer, 2015).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR70\">Hatamizadeh, A. et al. UNETR: transformers for 3D medical image segmentation. In Proc. 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision 574\u2013584 (IEEE, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR71\">Xue, C. et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 30, 2977\u20132989 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-024-03118-z\" data-track-item_id=\"10.1038\/s41591-024-03118-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-024-03118-z\" aria-label=\"Article reference 71\" data-doi=\"10.1038\/s41591-024-03118-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhsV2ntLvO\" aria-label=\"CAS reference 71\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38965435\" aria-label=\"PubMed reference 71\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11485262\" aria-label=\"PubMed Central reference 71\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 71\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=AI-based%20differential%20diagnosis%20of%20dementia%20etiologies%20on%20multimodal%20data&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-024-03118-z&amp;volume=30&amp;pages=2977-2989&amp;publication_year=2024&amp;author=Xue%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR72\">Yang, A. et al. Qwen3 technical report. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2505.09388\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2505.09388\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2505.09388<\/a> (2025).<\/p>\n","protected":false},"excerpt":{"rendered":"Sch\u00f6ckel, L. et al. Developments in X-ray contrast media and the potential impact on computed tomography. Invest. Radiol.&hellip;\n","protected":false},"author":2,"featured_media":322895,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[114,6982,4068,85,46,4069,141,160198],"class_list":["post-322894","post","type-post","status-publish","format-standard","has-post-thumbnail","category-business","tag-business","tag-computed-tomography","tag-humanities-and-social-sciences","tag-il","tag-israel","tag-multidisciplinary","tag-science","tag-three-dimensional-imaging"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/322894","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/comments?post=322894"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/322894\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media\/322895"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media?parent=322894"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/categories?post=322894"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/tags?post=322894"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}