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Case descriptions were entered into ChatGPT and Bard\u2019s interfaces with the question \u201cWhat kind of surgery would you perform?\u201d and repeated three times to analyze the answers\u2019 consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>ChatGPT surgical choice was consistent with those of glaucoma specialists in 35\/60 cases (58%), compared to 19\/60 (32%) of Gemini (<jats:italic>p<\/jats:italic>\u2009=\u20090.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In \u201cchallenging\u201d cases, ChatGPT agreed with specialists in 9\/20 choices (45%), outperforming Google Gemini performances (4\/20, 20%). Overall, GQS scores were 3.5\u2009\u00b1\u20091.2 and 2.1\u2009\u00b1\u20091.5 for ChatGPT and Gemini (<jats:italic>p<\/jats:italic>\u2009=\u20090.002). This difference was even more marked if focusing only on \u201cchallenging\u201d cases (1.5\u2009\u00b1\u20091.4 vs. 3.0\u2009\u00b1\u20091.5, <jats:italic>p<\/jats:italic>\u2009=\u20090.001).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00417-024-06470-5","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T09:12:20Z","timestamp":1712221940000},"page":"2945-2959","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison"],"prefix":"10.1007","volume":"262","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2979-1638","authenticated-orcid":false,"given":"Matteo Mario","family":"Carl\u00e0","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gloria","family":"Gambini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Baldascino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Boselli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Giannuzzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Margollicci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislao","family":"Rizzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"6470_CR1","volume-title":"Quick start guide to large language models: strategies and best practices for using ChatGPT and other LLMs","author":"S Ozdemir","year":"2023","unstructured":"Ozdemir S (2023) Quick start guide to large language models: strategies and best practices for using ChatGPT and other LLMs. Addison-Wesley Professional"},{"key":"6470_CR2","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW (2023) Large language models in medicine. 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