{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T02:06:33Z","timestamp":1793844393759,"version":"4.6.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01806-x","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T16:34:48Z","timestamp":1767630888000},"page":"4136-4144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Domain-Specific and Computer-Vision-Driven Versus General-Purpose AI Models in PA-CXR Analysis: a Comparative Study with Emergency-Medicine Specialists"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9456-8777","authenticated-orcid":false,"given":"Yunus","family":"Dogan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7204-6185","authenticated-orcid":false,"given":"Adem","family":"Az","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3365-3713","authenticated-orcid":false,"given":"Ozgur","family":"Sogut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1178-2274","authenticated-orcid":false,"given":"Tarik","family":"Akdemir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1325-3360","authenticated-orcid":false,"given":"Huseyin","family":"Ergenc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9852-3034","authenticated-orcid":false,"given":"Tuba Betul","family":"Umit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4365-8818","authenticated-orcid":false,"given":"Busra Nur","family":"Armagan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6829-5777","authenticated-orcid":false,"given":"Mustafa Fatih","family":"Ozturk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"1806_CR1","doi-asserted-by":"crossref","unstructured":"Bajwa J, Munir U, Nori A, Williams B: Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 8:e188-e194, 2021","DOI":"10.7861\/fhj.2021-0095"},{"key":"1806_CR2","doi-asserted-by":"crossref","unstructured":"Najjar R: Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 13:2760, 2023","DOI":"10.3390\/diagnostics13172760"},{"key":"1806_CR3","doi-asserted-by":"crossref","unstructured":"Yu J, Cheong IH, Kozlakidis Z, Wang H: Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China. Front Digit Health 7:1544520, 2025","DOI":"10.3389\/fdgth.2025.1544520"},{"key":"1806_CR4","doi-asserted-by":"crossref","unstructured":"Altinbilek E, Az A, Sogut O, Dogan Y, Akdemir T, Belen E, Biter HI, Saricicek T, Ozcomlekci M, Kilic N: Task-specific versus general-purpose AI models in ECG analysis: A comparative study with emergency medicine specialists. Am J Emerg Med 95:220-226, 2025","DOI":"10.1016\/j.ajem.2025.06.068"},{"key":"1806_CR5","doi-asserted-by":"crossref","unstructured":"Broder J: Imaging the Chest: The Chest Radiograph. Diagnostic Imaging for the Emergency Physician 185\u2013296, 2011","DOI":"10.1016\/B978-1-4160-6113-7.10005-5"},{"key":"1806_CR6","doi-asserted-by":"crossref","unstructured":"Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, Barrett R, Banu S, Creswell J: Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health 3:e543-e554, 2021","DOI":"10.1016\/S2589-7500(21)00116-3"},{"key":"1806_CR7","doi-asserted-by":"crossref","unstructured":"Lind Plesner L, M\u00fcller FC, Brejneb\u00f8l MW, Laustrup LC, Rasmussen F, Nielsen OW, Boesen M, Brun Andersen M: Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion. Radiology 308(3):e231236, 2023","DOI":"10.1148\/radiol.231236"},{"key":"1806_CR8","doi-asserted-by":"crossref","unstructured":"Albahli S, Nazir T: AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease. Front Med (Lausanne) 9:955765, 2022","DOI":"10.3389\/fmed.2022.955765"},{"key":"1806_CR9","unstructured":"OpenAI. GPT-5. OpenAI. San Francisco (CA): OpenAI; c2025. Available at https:\/\/openai.com\/gpt-5\/. Accessed 18 Sept 2025"},{"key":"1806_CR10","unstructured":"OpenAI. Xray-GPT. San Francisco (CA): OpenAI; c2025. Available at https:\/\/chatgpt.com\/g\/g-jxHNmCe49-xraygpt-your-assistant-radiologist. Accessed 18 Sept 2025"},{"key":"1806_CR11","unstructured":"Qure.ai. qXR becomes first AI-based chest X-ray interpretation tool to receive CE certification. Mumbai (IN): Qure.ai; 2020. Available at https:\/\/www.qure.ai\/news_press_coverages\/qxr-becomes-first-ai-based-chest-x-ray-interpretation-tool-to-receive-ce-certification. Accessed 18 Sept 2025"},{"key":"1806_CR12","doi-asserted-by":"crossref","unstructured":"Chang KC, Hsieh PH, Wu MY, Wang YC, Chen JY, Tsai FJ, Shih ESC, Hwang MJ, Huang TC: Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms. Can J Cardiol 37:94-104, 2021","DOI":"10.1016\/j.cjca.2020.02.096"},{"key":"1806_CR13","doi-asserted-by":"crossref","unstructured":"Hoppe JM, Auer MK, Str\u00fcven A, Massberg S, Stremmel C: ChatGPT with GPT-4 Outperforms Emergency Department Physicians in Diagnostic Accuracy: Retrospective Analysis. J Med Internet Res 26:e56110, 2024","DOI":"10.2196\/56110"},{"key":"1806_CR14","doi-asserted-by":"crossref","unstructured":"Arslan B, Eyupoglu G, Korkut S, Turkdogan KA, Altinbilek E: The accuracy of AI-assisted chatbots on the annual assessment test for emergency medicine residents. J Med Surg Public Health 3:100070, 2024","DOI":"10.1016\/j.glmedi.2024.100070"},{"key":"1806_CR15","doi-asserted-by":"crossref","unstructured":"Zhou Y, Ong H, Kennedy P, Wu CC, Kazam J, Hentel K, Flanders A, Shih G, Peng Y: Evaluating GPT-V4 (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs. Radiology 311:e233270, 2024","DOI":"10.1148\/radiol.233270"},{"key":"1806_CR16","doi-asserted-by":"crossref","unstructured":"Hayden N, Gilbert S, Poisson LM, Griffith B, Klochko C: Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions. Radiology 312:e240153, 2024","DOI":"10.1148\/radiol.240153"},{"key":"1806_CR17","doi-asserted-by":"crossref","unstructured":"Zavaleta-Monestel E, Quesada-Villase\u00f1or R, Arguedas-Chac\u00f3n S, Garc\u00eda-Montero J, Barrantes-L\u00f3pez M, Salas-Segura J, Anch\u00eda-Alfaro A, Nieto-Bernal D, Diaz-Juan DE: Revolutionizing Healthcare: Qure.AI\u2019s Innovations in Medical Diagnosis and Treatment. Cureus 16(6):e61585, 2024","DOI":"10.7759\/cureus.61585"},{"key":"1806_CR18","doi-asserted-by":"crossref","unstructured":"Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, Wu MY, Zhang EW, Muse V, Miller B, Sabzalipour F, Bizzo BC, Dreyer KJ, Kaviani P, Digumarthy SR, Kalra MK: Association of Artificial Intelligence-Aided Chest Radiograph Interpretation with Reader Performance and Efficiency. JAMA Netw Open 5:e2229289, 2022","DOI":"10.1001\/jamanetworkopen.2022.29289"},{"key":"1806_CR19","doi-asserted-by":"crossref","unstructured":"Kaviani P, Digumarthy SR, Bizzo BC, Reddy B, Tadepalli M, Putha P, Jagirdar A, Ebrahimian S, Kalra MK, Dreyer KJ: Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study. Diagnostics (Basel) 12:2086, 2022","DOI":"10.3390\/diagnostics12092086"},{"key":"1806_CR20","doi-asserted-by":"crossref","unstructured":"Bassam M, Manoj T, Tarun R, Rajalakshmi S, Mahmood Yaseen H, Usama B, Rifat H, Ehsan H, Abdullah A: Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy. Adv Biomed Health Sci 1:137\u201343, 2022","DOI":"10.4103\/abhs.abhs_17_22"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01806-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01806-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01806-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:17:07Z","timestamp":1792786627000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01806-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["1806"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01806-x","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"assertion":[{"value":"15 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Ethical approval was granted by the Institutional Review Board of Haseki Training and Research Hospital, Istanbul, T\u00fcrkiye (approval number: 31\u20132025). All participants provided written informed consent before their inclusion in the study.","order":1,"name":"Ethics","label":"Ethics Approval and Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}