{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T13:19:08Z","timestamp":1787750348306,"version":"build-2784847793"},"reference-count":8,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To evaluate the accuracy and reliability of four artificial intelligence (AI) models\u2014ChatGPT, Copilot, DeepSeek, and Gemini\u2014in generating PubMed citations for literature related to lens disease, cataracts, iris disorders, and anterior chamber pathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>Comparative accuracy assessment study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      Forty standardized clinical paragraphs from The Review of Ophthalmology (4\n                      <jats:sup>th<\/jats:sup>\n                      edition) were used as test inputs. Each AI model was prompted to generate AMA-11\u2013style PubMed references. Citation accuracy was assessed using predefined criteria, including PubMed verifiability, DOI concordance, and bibliographic accuracy. Two expert reviewers independently classified the citations as fully cited, partially cited, or not cited, and assessed inter-rater reliability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The citation accuracy varied significantly among the models. DeepSeek demonstrated the highest accuracy (52.5%), followed by ChatGPT (32.5%) and Copilot (20.0%), whereas Gemini demonstrated the lowest accuracy (2.5%) (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.001). DOI mismatches were the most common errors across all models. Expert validation confirmed these findings, with DeepSeek producing the highest number of fully cited references. Inter-rater agreement was substantial (Cohen\u2019s \u03ba = 0.65).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Domain-specific AI models, particularly DeepSeek, outperform general-purpose models in generating PubMed citations from ophthalmic literature. However, all the evaluated models exhibited citation errors, underscoring the necessity of human verification. AI tools may enhance academic workflows as assistive systems but should not be used autonomously for reference generation in medical research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijo.ijo_3155_25","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:05:06Z","timestamp":1782749106000},"page":"1073-1076","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["The role of artificial intelligence in academic citation: A study on lens, cataract, and anterior segment research"],"prefix":"10.4103","volume":"74","author":[{"given":"Mustafa","family":"Civelekler","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, University of Health Sciences, Ankara Etlik City Hospital, Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet","family":"\u00c7\u0131t\u0131r\u0131k","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Health Sciences, Ankara Etlik City Hospital, Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"R1-20260630","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s10462-022-10246-w","article-title":"Human-in-the-loop machine learning: A state of the art","volume":"56","author":"Mosqueira-Rey","year":"2023","journal-title":"Artif Intell Rev"},{"key":"R2-20260630","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.amjmed.2023.06.012","article-title":"Hallucinations in ChatGPT: A cautionary tale for biomedical researchers","volume":"136","author":"Goddard","year":"2023","journal-title":"Am J Med"},{"key":"R5-20260630","doi-asserted-by":"crossref","first-page":"78","DOI":"10.4103\/joco.joco_192_23","article-title":"Evaluation and comparison of the knowledge levels of current artificial intelligence programs on retinal\/vitreous diseases and treatment methods","volume":"36","author":"Sensoy","year":"2024","journal-title":"J Curr Ophthalmol"},{"key":"R6-20260630","doi-asserted-by":"crossref","first-page":"e53164","DOI":"10.2196\/53164","article-title":"Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: Comparative analysis","volume":"26","author":"Chelli","year":"2024","journal-title":"J Med Internet Res"},{"key":"R7-20260630","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1007\/s00405-023-08441-8","article-title":"Accuracy of ChatGPT-3.5 and -4 in providing scientific references in otolaryngology\u2013head and neck surgery","volume":"281","author":"Lechien","year":"2024","journal-title":"Eu Arch Otorhinolaryngol"},{"key":"R8-20260630","first-page":"e37432","article-title":"Exploring the boundaries of reality: Investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references","volume":"15","author":"Athaluri","year":"2023","journal-title":"Cureus"},{"key":"R9-20260630","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.jclinepi.2021.01.006","article-title":"An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials","volume":"133","author":"Noel-Storr","year":"2021","journal-title":"J Clin Epidemiol"},{"key":"R10-20260630","first-page":"652","article-title":"PubMed retrieval with RAG techniques","volume":"316","author":"Thomo","year":"2024","journal-title":"Stud Health Technol Inform"}],"container-title":["Indian Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/IJO.IJO_3155_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T12:26:31Z","timestamp":1787747191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/IJO.IJO_3155_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"references-count":8,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/ijo.ijo_3155_25","relation":{},"ISSN":["0301-4738","1998-3689"],"issn-type":[{"value":"0301-4738","type":"print"},{"value":"1998-3689","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]},"assertion":[{"value":"2025-12-09","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-03","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}