ReviewOman journal of ophthalmology
Large language models and eye education: A narrative review.
Review in Oman journal of ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of large language models (LLMs) has sparked both excitement and concern due to their rapidly advancing capabilities. This development marks a crucial moment in health care, as LLMs have the potential to bring about revolutionary changes to current clinical practices and ultimately patient care. ChatGPT, Gemini (formerly known as Bard), among other applications, are all examples of publicly available generative artificial intelligence (AI) chatbots based on LLM technology. This narrative review aims to outline the literature regarding the use of publicly available LLMs by different stakeholders, such as patients, clinicians, and clinical students, to enhance their understanding of ocular topics, core implications of such technologies for both patients and clinicians, and areas in which further research is necessary to fully appreciate these tools' impact and for its safe integration in clinical eye care.
Indexed as
Identifiers
What OpenQuestion holds
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.