The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index, and 1 are not checked yet.
Evaluation and Comparison of Ophthalmic Scientific Abstracts and References by Current Artificial Intelligence Chatbots
Open accessAbstract
Importance: Language-learning model–based artificial intelligence (AI) chatbots are growing in popularity and have significant implications for both patient education and academia. Drawbacks of using AI chatbots in generating scientific abstracts and reference lists, including inaccurate content coming from hallucinations (ie, AI-generated output that deviates from its training data), have not been fully explored. Objective: To evaluate and compare the quality of ophthalmic scientific abstracts and references generated by earlier and updated versions of a popular AI chatbot. Design, Setting, and Participants: This cross-sectional comparative study used 2 versions of an AI chatbot to generate scientific abstracts and 10 references for clinical research questions across 7 ophthalmology subspecialties. The abstracts were graded by 2 authors using modified DISCERN criteria and performance evaluation scores. Main Outcome and Measures: Scores for the chatbot-generated abstracts were compared using the t test. Abstracts were also evaluated by 2 AI output detectors. A hallucination rate for unverifiable references generated by the earlier and updated versions of the chatbot was calculated and compared. Results: The mean modified AI-DISCERN scores for the chatbot-generated abstracts were 35.9 and 38.1 (maximum of 50) for the earlier and updated versions, respectively ( P = .30). Using the 2 AI output detectors, the mean fake scores (with a score of 100% meaning generated by AI) for the earlier and updated chatbot-generated abstracts were 65.4% and 10.8%, respectively ( P = .01), for one detector and were 69.5% and 42.7% ( P = .17) for the second detector. The mean hallucination rates for nonverifiable references generated by the earlier and updated versions were 33% and 29% ( P = .74). Conclusions and Relevance: Both versions of the chatbot generated average-quality abstracts. There was a high hallucination rate of generating fake references, and caution should be used when using these AI resources for health education or academic purposes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our findings align with those of Hua et al [25] who evaluated hallucinations in AI-generated ophthalmic scientific abstracts and references.”
From Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study (2024) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our findings showed variations in the RHS across different AI chatbots, ranging from almost null for SciSpace and Elicit to a critically high degree of hallucination for ChatGPT [25].”
From Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study (2024) · cites this paper as [25]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Charnock, D., Shepperd, S., Needham, G., & Gann, R. (1999) · Journal of Epidemiology and Community Health · doi:10.1136/jech.53.2.105 · Cited by 3,329
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
A Randomized, Controlled Trial of Corticosteroids in the Treatment of Acute Optic Neuritis
Beck, R. W., Cleary, P. A., Anderson, M. M., Keltner, J. L., Shults, W. T., Kaufman, D. I., Buckley, E. G., Corbett, J. J., Kupersmith, M. J., Miller, N. R., et al. (1992) · New England Journal of Medicine · doi:10.1056/nejm199202273260901 · Cited by 1,349
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605
Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585
Falke, T., Ribeiro, L. F. R., Utama, P. A., Dagan, I., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1213 · Cited by 265
Research works printed but not linked: 2
- Sage. ChatGPT and generative AI. Accessed May 18, 2023. https://us.sagepub.com/en-us/nam/chatgpt-and-generative-ai
- Elsevier. Publishing ethics—the use of generative AI and AI-assisted technologies in scientific writing. Accessed May 18, 2023. https://www.elsevier.com/about/policies/publishing-ethics
Cited by
Sorted by citations
Aljamaan, F., Temsah, M.-H., Altamimi, I., Al-Eyadhy, A., Jamal, A., Alhasan, K., Mesallam, T. A., Farahat, M., & Malki, K. H. (2024) · JMIR Medical Informatics · doi:10.2196/54345 · Cited by 211
“Our findings showed variations in the RHS across different AI chatbots, ranging from almost null for SciSpace and Elicit to a critically high degree of hallucination for ChatGPT [25].”
BackgroundResultRevolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
Zhang, K., Meng, X., Yan, X., Ji, J., Liu, J., Xu, H., Zhang, H., Liu, D., Wang, J., Wang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/59069 · Cited by 148
Williamson, S. M., & Prybutok, V. (2024) · Information · doi:10.3390/info15060299 · Cited by 146
Democratizing Financial Knowledge with ChatGPT by OpenAI: Unleashing the Power of TechnologyPreprint
Yue, T., Au, D., Au, C. C., & Iu, K. Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4346152 · Cited by 74
Utility of artificial intelligence‐based large language models in ophthalmic care
Biswas, S., Davies, L. N., Sheppard, A. L., Logan, N. S., & Wolffsohn, J. S. (2024) · Ophthalmic and Physiological Optics · doi:10.1111/opo.13284 · Cited by 61
Using Large Language Models to Generate Educational Materials on Childhood Glaucoma
Dihan, Q. A., Chauhan, M. Z., Eleiwa, T. K., Hassan, A. K., Sallam, A. B., Khouri, A. S., Chang, T. C., & Elhusseiny, A. M. (2024) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2024.04.004 · Cited by 55
Ittarat, M., Cheungpasitporn, W., & Chansangpetch, S. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13121679 · Cited by 50
Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think like an Ophthalmologist?
Chen, J., Reddy, A. J., Al-Sharif, E. M., Shoji, M. K., Kalaw, F. G. P., Eslani, M., Lang, P. Z., Arya, M., Koretz, Z. A., Bolo, K. A., et al. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100600 · Cited by 45
Evaluating Large Language Models in Ophthalmology: Systematic Review
Zhang, Z., Zhang, H., Pan, Z., Bi, Z., Wan, Y., Song, X., & Fan, X. (2025) · Journal of Medical Internet Research · doi:10.2196/76947 · Cited by 38
Harnessing AI Chatbots for EFL Essay Writing: A Paradigm Shift in Language Pedagogy
Pitychoutis, K. M. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.13 · Cited by 36
Flitcroft, M. A., Sheriff, S. A., Wolfrath, N., Maddula, R., McConnell, L., Xing, Y., Haines, K., Wong, S. L., & Kothari, A. N. (2024) · Annals of Surgical Oncology · doi:10.1245/s10434-024-15549-6 · Cited by 35
The trust crisis in artificial intelligence: AI hallucinations and human-AI collaboration
Cheng, Q., Dai, Y., Liu, X., & Peng, S. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103286 · Cited by 33
Generative artificial intelligence and academic writing: friend or foe?
Cohen, J. F., & Moher, D. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111646 · Cited by 32
Large language models: a new frontier in paediatric cataract patient education
Dihan, Q. A., Chauhan, M. Z., Eleiwa, T. K., Brown, A. D., Hassan, A. K., Khodeiry, M. M., ElSheikh, R. H., Oke, I., Nihalani, B. R., VanderVeen, D. K., et al. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2024-325252 · Cited by 31
Embracing Generative Artificial Intelligence in Clinical Research and Beyond
Foote, H. P., Hong, C., Anwar, M. M., Borentain, M., Bugin, K., Dreyer, N. A., Fessel, J. P., Goyal, N., Hanger, M., Hernandez, A. F., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101593 · Cited by 36
Wong, M., Lim, Z. W., Pushpanathan, K., Cheung, C. Y.-L., Wang, Y. X., Chen, D. Z., & Tham, Y. C. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-324734 · Cited by 30
Can we trust academic AI detective? Accuracy and limitations of AI-output detectors
Erol, G., Ergen, A., Gülşen Erol, B., Kaya Ergen, Ş., Bora, T. S., Çölgeçen, A. D., Araz, B., Şahin, C., Bostancı, G., Kılıç, İ., et al. (2025) · Acta Neurochirurgica · doi:10.1007/s00701-025-06622-4 · Cited by 28
ChatGPT References Unveiled: Distinguishing the Reliable from the Fake
Giray, L. (2024) · Internet Reference Services Quarterly · doi:10.1080/10875301.2023.2265369 · Cited by 26
Artificial Intelligence (AI) in the Financial Sector
Riani, R. (2024) · Digital Economics Review. · doi:10.58968/der.v1i1.476 · Cited by 25
Large Language Models in Ophthalmology: A Review of Publications from Top Ophthalmology Journals
Agnihotri, A. P., Nagel, I. D., Artiaga, J. C. M., Guevarra, M. C. B., Sosuan, G. M. N., & Kalaw, F. G. P. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100681 · Cited by 24
Sun, Z., Yim, W.-W., Uzuner, Ö., Xia, F., & Yetişgen, M. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104866 · Cited by 20
Momenaei, B., Mansour, H. A., Kuriyan, A. E., Xu, D. N., Sridhar, J., Ting, D. S. W., & Yonekawa, Y. (2024) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000001036 · Cited by 19
Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia
Delsoz, M., Hassan, A. K., Nabavi, A., Rahdar, A., Fowler, B., Kerr, N. C., Ditta, L. C., Hoehn, M. E., DeAngelis, M. M., Grzybowski, A. E., et al. (2025) · Ophthalmology and Therapy · doi:10.1007/s40123-025-01142-x · Cited by 18
Ethical Issues in AI-Generated Texts: A Systematic Review and Analysis
Zhang, Y., Zhou, T., Qiao, H., & Li, T. X. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2530071 · Cited by 17
Large language models and the future of rheumatology: assessing impact and emerging opportunities
Mannstadt, I., & Mehta, B. Y. (2023) · Current Opinion in Rheumatology · doi:10.1097/bor.0000000000000981 · Cited by 17
PERFORMANCE ASSESSMENT OF AN ARTIFICIAL INTELLIGENCE CHATBOT IN CLINICAL VITREORETINAL SCENARIOS
Maywood, M. J., Parikh, R., Deobhakta, A. A., & Begaj, T. (2024) · Retina · doi:10.1097/iae.0000000000004053 · Cited by 15
Ethical considerations for large language models in ophthalmology
Kalaw, F. G. P., & Baxter, S. L. (2024) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000001083 · Cited by 12
Xu, Q., Wang, J., Chen, X., Wang, J., Li, H., Wang, Z., Li, W., Gao, J., Chen, C., & Gao, Y. (2025) · Journal of Medical Systems · doi:10.1007/s10916-024-02129-0 · Cited by 12
Ahn, S. (2025) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2448278.139 · Cited by 13
Impact of artificial intelligence on future clinical pharmacy research and scholarship
Chan, A., Baker, W. L., Abazia, D. T., Bauman, J. L., DeVane, C. L., Goodlet, K. J., Hall, N., Hicks, J. K., Jones, E., Lu, C., et al. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70003 · Cited by 11
Karnan, N., Francis, J., Vijayvargiya, I., & Tan, C. R. (2024) · Cureus · doi:10.7759/cureus.74398 · Cited by 10
Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Antaki, F., Mikhail, D., Milad, D., Mammo, D. A., Sharma, S. K., Srivastava, S. K., Chen, B. Y., Touma, S., Sevgi, M., El‐Khoury, J., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.101034 · Cited by 10
Self-citationLeopold, S. S., Briars, C. E., Gebhardt, M. C., Gioe, T. J., Manner, P. A., Porcher, R., Rimnac, C. M., & Wongworawat, M. D. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003084 · Cited by 10
He, Z., Zhao, L., Li, G., Wang, J., Cai, S., Tu, P., Chen, J., Wu, J., Zhang, J., Chen, R., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1670510 · Cited by 8
AI discernment in foot and ankle surgery research: A survey investigation
Cooperman, S. R., Olaniyan, A., & Brandão, R. A. (2024) · Foot and Ankle Surgery · doi:10.1016/j.fas.2024.10.001 · Cited by 8
Scientific writing in the age of artificial intelligence: trust on trial?
Vaishya, R., Misra, A., & Vaish, A. (2025) · Postgraduate Medical Journal · doi:10.1093/postmj/qgaf215 · Cited by 7
Popescu-Apreutesei, L.-E., Iosupescu, M.-S., Necula, S.-C., & Păvăloaia, V.-D. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.064747 · Cited by 7
Chatbots, Artificial Intelligence, and the Future of Scientific Reporting
Volpe, N. J., & Mirza, R. G. (2023) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2023.3344 · Cited by 6
Yao, L., Gu, T., Li, X., Jiao, Y., Li, M., Graff, J. C., Li, Y., & Gu, W. (2025) · Die Naturwissenschaften · doi:10.1007/s00114-025-02036-5 · Cited by 5
Dubois, C., Dawit, H., Korevaar, D. A., Vali, Y., Sibaaie, A. A., Frank, R. A., McInnes, M. D. F., Hyde, C., Bossuyt, P. M., & Cohen, J. F. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111459 · Cited by 5
Goodman, M. A., Lee, A., Schreck, Z., & Hollman, J. H. (2025) · Journal of Physical Therapy Education · doi:10.1097/jte.0000000000000396 · Cited by 5
Generative chatbots in headache education and research: A narrative review
Romozzi, M., García‐Azorín, D., Rubio‐Beltrán, E., & Labastida‐Ramírez, A. (2025) · Cephalalgia · doi:10.1177/03331024251372117 · Cited by 4
Evaluating Text-to-Image Generation in Pediatric Ophthalmology
Jong, S., Dihan, Q. A., Khodeiry, M. M., Alzein, A. F., Scelfo, C., & Elhusseiny, A. M. (2025) · Journal of Pediatric Ophthalmology & Strabismus · doi:10.3928/01913913-20250724-03 · Cited by 4
Chatbots in neurology and neuroscience: Interactions with students, patients and neurologists
Sandrone, S. (2024) · Brain Disorders · doi:10.1016/j.dscb.2024.100145 · Cited by 4
See, Y. K. C., Lim, K. S. A., Yung, A. W., Charlene, C. S. Y., Fan, X., & Li, K. Z. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9060151 · Cited by 4
Oh, J. (2026) · Journal of Korean Medical Science · doi:10.3346/jkms.2026.41.e298 · Cited by 3
Velasco, A. M., Coelho, E. W. F., Paes, V. L. S., Castro, R. C., & de Oliveira, R. D. C. S. (2025) · Caderno Pedagógico · doi:10.54033/cadpedv22n5-216 · Cited by 2
Scientific Data Fabrication and AI—Pandora’s Box
Hua, H.-U. (2025) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2025.0936 · Cited by 2
Self-citationAccuracy of ChatGPT for literature citations in lower limb arthroplast
Johnson, M. C., Magruder, M. L., Hahn, A. K., Tabbaa, A., Dennis, E., & Grosso, M. J. (2025) · Journal of Orthopaedic Reports · doi:10.1016/j.jorep.2025.100756 · Cited by 2
Characteristics of Retracted Publications in the Ophthalmology Literature
Bala, S., Regueiro, M., Bellanda, V. C. F., Parel, J. G., Barbosa, G. C. S., Patel, K. R., & Mammo, D. A. (2026) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2026.0630 · Cited by 2
Self-citationApplying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles
Foote, H. P., Cohen‐Wolkowiez, M., Lindsell, C. J., & Hornik, C. P. V. (2024) · The Journal of Pediatric Pharmacology and Therapeutics · doi:10.5863/1551-6776-29.3.336 · Cited by 2
Integrating domain-specific resources: Advancing AI for foot and ankle surgery
Cooperman, S. R., & Brandão, R. A. (2024) · Foot & Ankle Surgery Techniques Reports & Cases · doi:10.1016/j.fastrc.2024.100445 · Cited by 2
Fransız Kültür Edincinin Kazandırılmasında Yapay Zekâ Rehber Olabilir Mi? Bing Chat AI Örneği
Yalçin, P., & Yağcı, Ş. Ç. (2024) · Journal of Higher Education and Science · doi:10.5961/higheredusci.1402198 · Cited by 1
Yüksel, G., & Gürkan, S. (2025) · Journal of Health Sciences and Medicine · doi:10.32322/jhsm.1752483 · Cited by 1
Hassanzadeh, T., Lee, A. H., Gupta, N., Garcia‐Rodriguez, L. R., Tower, J. I., Alperovich, M. A., & Spiegel, J. H. (2025) · Facial Plastic Surgery & Aesthetic Medicine · doi:10.1177/26893614251405800 · Cited by 1
Assessing the Critical Failure Factors of AI Chatbots for Research Using ISM Approach
Gabia, C. C., Gabia, D. A., Villa, S. C., Villa, B., Nolon, N. F., Mamites, I. O., & Himang, M. M. (2026) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.402395 · Cited by 1
Evaluation Methods for AI in Public Health
Wu, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15872-7_4 · Cited by 1
Blinded by the Bot: Benchmarking GPT and Gemini Against Human Authors in Otolaryngology Reviews
Hack, S., Attal, R., Nitro, L., Rosso, C., Urbanelli, A., Bulfamante, A. M., Vaira, L. A., Ahmed, O. G., & Takashima, M. (2026) · World Journal of Otorhinolaryngology - Head and Neck Surgery · doi:10.1002/wjo2.70131 · Cited by 1
Agentic T-STROBE: a front-shifted STROBE paradigm for observational research
Bo, C., Li, R., 赵敬华, Feng, S., Ji, X., & Xue, F. (2026) · Statistics Innovation · doi:10.48130/stati-0026-0014 · Cited by 1
Goggin, M., & Chen, F. K. (2024) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.14350 · Cited by 1
Generative artificial intelligence: A hot topic to face with
Cosci, F., & Mikocka‐Walus, A. A. (2025) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2025.112113 · Cited by 1
The potential misuse of AI in online medical admission interviewsPreprint
Hampshire, M., Howard, J., & James, D. K. (2024) · Research Square · doi:10.21203/rs.3.rs-4149316/v1
Musings on AI Hallucinations and Reading Your References
Alweis, R. L. (2024) · Advances in Clinical Medical Research and Healthcare Delivery · doi:10.53785/2769-2779.1240
Artificial Intelligence Chatbot Use in Ophthalmology
Lyons, R. J., & Arepalli, S. (2024) · touchREVIEWS in Ophthalmology · doi:10.17925/usor.2024.18.1.3
Evaluation of artificial intelligence generated responses of commonly asked ophthalmic queries
Babu, M., Sumanth, B., & Shruthi, K. (2025) · Kerala Journal of Ophthalmology · doi:10.4103/kjo.kjo_26_25
Oliva, L. E. A., & May-Guillermo, E. G. (2026) · Emerging Trends in Education · doi:10.19136/etie.v8n16.6338
Wolverton, C. C. (2026) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-01-2026-269
Surgical innovation and technology
Matthews, J. B., Ruurda, J. P. H., Vaughan-Shaw, P. G., Winter, D. C., Beggs, A. D., Yiu, A., Davidson, B. R., Seeliger, B., Chen, X., Stoyanov, D. V., et al. (2025) · British journal of surgery · doi:10.1093/bjs/znaf224
Importance of AI Chatbot in Nursing Education: A Systematic Review
Ghaffar, T., Balakrishnan, R., & Javaid, M. A. A. (2026) · Biological and Clinical Sciences Research Journal · doi:10.54112/bcsrj.v7i4.2271
Generative AI Chatbot Responses to Suicide and Self-Harm: A Systematic Review (Preprint)
Kelly, D. A., Franz, P. J., Gutman, A., Sleiman, M., Dorsky, J., Singh, H., Appel, W., Wyatt, S. M., Low, D. M., Stirman, S. W., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/103517
Rezaeian, M. (2025) · مجله علمی دانشگاه علوم پزشکی رفسنجان · doi:10.66224/jrums.24.9.773
Rezaeian, M. (2026) · مجله علمی دانشگاه علوم پزشکی رفسنجان · doi:10.66224/jrums.24.10.887
Evaluating Large Language Models to Improve Spanish Patient Education on Childhood Glaucoma
Tyagi, M., Dihan, Q. A., Alzein, A. F., Salamanca, O. C., Rahal, D. A., Vila-Delgado, M., Sanvicente, C., Khodeiry, M. M., Elnahry, A. G., Rolim-de-Moura, C., et al. (2026) · Journal of Pediatric Ophthalmology & Strabismus · doi:10.3928/01913913-20260416-02
Olulana, K., Owolawi, P. A., Du, C., & Abe, B. T. (2026) · Journal of Intelligent Systems and Applied Data Science · doi:10.64680/jisads.v4i1.65
Sahhar, M., Lewis, A. M., Achilova, F., Singh, M., Nassar, J. E., Callanan, T. C., Liu, J., Daniels, A. H., & Antoci, V. (2026) · The Journal of Arthroplasty · doi:10.1016/j.arth.2026.07.041
Real-World Heterogeneity of Postretraction Citations—Reply
Bellanda, V. C. F., Bala, S., & Mammo, D. A. (2026) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2026.3215
Self-citationNasution, M., & Yustina, I. (2025) · Journal of Digital Health Innovation and Medical Technology
Hernández, I., Souza, D., Toro, C. D., Parker, K., Falcon, A., Danton, G. H., Subhawong, T. K., & Alessandrino, F. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.09.042
Jun, H. W., Nam, D., Choi, J. Y., & Yoo, T. K. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116609
Metadata
- Authors
- Hua, H.-U., Kaakour, A.-H., Rachitskaya, A., Srivastava, S., Sharma, S., & Mammo, D. A.
- Affiliations
- Cleveland Clinic (US)
- Corresponding author
- Mammo, Danny A.
- ORCID
- Venue
- JAMA Ophthalmology
- Publisher
- American Medical Association (AMA)
- Published
- 2023-09-01
- Volume
- 141
- Issue
- 9
- Pages
- 819
- ISSN
- 2168-6165 (ISSN-L)
- PMC
- PMC10375387
- OpenAlex
- W4385294579
- Cited by
- 76
- References
- 14
- Citations by year
- 2026: 16 · 2025: 28 · 2024: 26 · 2023: 6
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA Ophthalmology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Social Sciences › Education › Academic integrity and plagiarism
- Keywords
- AI chatbots, large language models, ophthalmology, hallucinations, DISCERN criteria, AI-generated content quality, ophthalmology subspecialties
- MeSH terms
- Artificial Intelligence, Cross-Sectional Studies, Eye, Hallucinations, Health Education, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
