Large Language Models in Ophthalmology: A Review of Publications from Top Ophthalmology Journals
Bibliographic record
Abstract
Purpose: To review and evaluate the current literature on the application and impact of large language models (LLMs) in the field of ophthalmology, focusing on studies published in high-ranking ophthalmology journals. Design: This is a retrospective review of published articles. Participants: This study did not involve human participation. Methods: Articles published in the first quartile (Q1) of ophthalmology journals on Scimago Journal & Country Rank discussing different LLMs up to June 7, 2024, were reviewed, parsed, and analyzed. Main Outcome Measures: All available articles were parsed and analyzed, which included the article and author characteristics and data regarding the LLM used and its applications, focusing on its use in medical education, clinical assistance, research, and patient education. Results: There were 35 Q1-ranked journals identified, 19 of which contained articles discussing LLMs, with 101 articles eligible for review. One-third were original investigations (32%; 32/101), with an average of 5.3 authors per article. The United States (50.4%; 51/101) was the most represented country, followed by the United Kingdom (25.7%; 26/101) and Canada (16.8%; 17/101). ChatGPT was the most used LLM among the studies, with different versions discussed and compared. Large language model applications were discussed relevant to their implications in medical education, clinical assistance, research, and patient education. Conclusions: The numerous publications on the use of LLM in ophthalmology can provide valuable insights for stakeholders and consumers of these applications. Large language models present significant opportunities for advancement in ophthalmology, particularly in team science, education, clinical assistance, and research. Although LLMs show promise, they also show challenges such as performance inconsistencies, bias, and ethical concerns. The study emphasizes the need for ongoing artificial intelligence improvement, ethical guidelines, and multidisciplinary collaboration. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.057 | 0.046 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".