{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:53:26Z","timestamp":1791914006822,"version":"4.3.4"},"reference-count":14,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["journalofnuclearcardiology.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Nuclear Cardiology"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.nuclcard.2026.102973","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:02:00Z","timestamp":1791910920000},"page":"102973","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Artificial intelligence passed the boards \u2014 But not the eye exam"],"prefix":"10.1016","volume":"63","author":[{"given":"Marcelo F.","family":"Di Carli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.nuclcard.2026.102973_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102946","article-title":"Retrieval-augmented Claude Opus 4.7 and GPT-5.5 surpass human performance on the Nuclear Cardiology Board Preparation Exam (and Claude drafts a paper about it)","volume":"63","author":"Killekar","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib2","article-title":"Evaluating AI proficiency in nuclear cardiology: large language models take on the board preparation exam","author":"Builoff","year":"2024","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib3","article-title":"Cardiac motion correction with a deep learning network for perfusion defect assessment in single-photon emission computed tomography myocardial perfusion imaging","author":"Zhang","year":"2024","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2025.102226","article-title":"Assessment of sestamibi CZT-SPECT reconstructed using deep-learning-based virtual attenuation correction maps according to coronary artery territory and with comparison to rubidium-PET","author":"Sprauel","year":"2025","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib5","article-title":"Artificial Intelligence-enabled parametric mapping of myocardial blood flow with (82)Rb PET compared to (18)F-flurpiridaz perfusion imaging: a paired pilot study of image quality","author":"Moulton","year":"2025","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib6","article-title":"Improving prognostic risk assessment of cardiovascular events with machine learning: an evaluation using positron emission tomography myocardial perfusion imaging","author":"Alahdab","year":"2025","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib7","first-page":"1768","article-title":"Explainable deep learning improves physician interpretation of myocardial perfusion imaging","volume":"63","author":"Miller","year":"2022","journal-title":"J Nucl Med"},{"key":"10.1016\/j.nuclcard.2026.102973_bib8","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyg.2024.1422177","article-title":"Do patients prefer a human doctor, artificial intelligence, or a blend, and is this preference dependent on medical discipline? Empirical evidence and implications for medical practice","volume":"15","author":"Riedl","year":"2024","journal-title":"Front Psychol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102775","article-title":"Radiation dosimetry of (15)O-water based on dynamic large-axial field-of-view positron emission tomography","volume":"63","author":"Lubberink","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102822","article-title":"Incremental prognostic value of the hemodynamic gain index in patients undergoing exercise stress myocardial perfusion imaging","volume":"63","author":"Yang","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102960","article-title":"Myocardial edema, blood flow, and contractility in Takotsubo syndrome: comparative evaluation with CMR and PET","volume":"63","author":"Kadoya","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102962","article-title":"Relapse in cardiac sarcoidosis: patterns and predictors on serial 18F-FDG PET imaging","volume":"63","author":"Gerodias","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102654","article-title":"Physiological differences in bone uptake of [(99m)Tc]Tc-pyrophosphate in men and women undergoing cardiac scintigraphy","volume":"63","author":"Akgun","year":"2026","journal-title":"J Nucl Cardiol"},{"key":"10.1016\/j.nuclcard.2026.102973_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.nuclcard.2026.102800","article-title":"Immuno-positron emission tomography in cardiovascular disease: advances, challenges, and opportunities","volume":"63","author":"Lie","year":"2026","journal-title":"J Nucl Cardiol"}],"container-title":["Journal of Nuclear Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1071358126003545?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1071358126003545?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:29:44Z","timestamp":1791912584000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1071358126003545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":14,"alternative-id":["S1071358126003545"],"URL":"https:\/\/doi.org\/10.1016\/j.nuclcard.2026.102973","relation":{},"ISSN":["1071-3581"],"issn-type":[{"value":"1071-3581","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence passed the boards \u2014 But not the eye exam","name":"articletitle","label":"Article Title"},{"value":"Journal of Nuclear Cardiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.nuclcard.2026.102973","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc. on behalf of American Society of Nuclear Cardiology.","name":"copyright","label":"Copyright"}],"article-number":"102973"}}