ReviewEye (London, England)2024
ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology.
Review in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Explainable neuro-symbolic artificial intelligence for automated interpretation of corneal topography and early keratoconus detection.Frontiers in artificial intelligence · 2026Article
- Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026Article
- Comparison of ChatGPT-4o and DeepSeek R1 in the Management of Ophthalmological Emergencies-An Analysis of Ten Fictional Case Vignettes.Journal of clinical medicine · 2025Article
- Explainable Artificial Intelligence Framework for Predicting Treatment Outcomes in Age-Related Macular Degeneration.Sensors (Basel, Switzerland) · 2025Article
- Benchmark analysis of myopia-related issues using large language models: a comparison of ChatGPT-4o and deepseek.BMC ophthalmology · 2025Article
- Review
- An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Comparative Assessment of Large Language Models in Optics and Refractive Surgery: Performance on Multiple-Choice Questions.Vision (Basel, Switzerland) · 2025Article
- Article
- Evaluation of Responses to Questions About Keratoconus Using ChatGPT-4.0, Google Gemini and Microsoft Copilot: A Comparative Study of Large Language Models on Keratoconus.Eye & contact lens · 2025Article
- The use of artificial intelligence based chat bots in ophthalmology triage.Eye (London, England) · 2025Article
- Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison.Ophthalmology and therapy · 2025Article
- Evaluating Large Language Models for Accuracy and Completeness of Vitiligo Patient Education: A Comparative Analysis.Clinical, cosmetic and investigational dermatology · 2025Article
- Clinical decision-making for uveal melanoma radiotherapy: comparative performance of experienced radiation oncologists and leading generative AI models.Frontiers in oncology · 2025Article
- A comparative study of GPT-4o and human ophthalmologists in glaucoma diagnosis.Scientific reports · 2024Observational
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ChatGPT, an artificial intelligence (AI) chatbot built on large language models (LLMs), has rapidly gained popularity. The benefits and limitations of this transformative technology have been discussed across various fields, including medicine. The widespread availability of ChatGPT has enabled clinicians to study how these tools could be used for a variety of tasks such as generating differential diagnosis lists, organizing patient notes, and synthesizing literature for scientific research. LLMs have shown promising capabilities in ophthalmology by performing well on the Ophthalmic Knowledge Assessment Program, providing fairly accurate responses to questions about retinal diseases, and in generating differential diagnoses list. There are current limitations to this technology, including the propensity of LLMs to "hallucinate", or confidently generate false information; their potential role in perpetuating biases in medicine; and the challenges in incorporating LLMs into research without allowing "AI-plagiarism" or publication of false information. In this paper, we provide a balanced overview of what LLMs are and introduce some of the LLMs that have been generated in the past few years. We discuss recent literature evaluating the role of these language models in medicine with a focus on ChatGPT. The field of AI is fast-paced, and new applications based on LLMs are being generated rapidly; therefore, it is important for ophthalmologists to be aware of how this technology works and how it may impact patient care. Here, we discuss the benefits, limitations, and future advancements of LLMs in patient care and research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.