ArticleJournal of clinical medicine2024
A Performance Evaluation of Large Language Models in Keratoconus: A Comparative Study of ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity.
Article in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Utility of large language models as information tools for nursing care in gout: a comparative study of DeepSeek and ChatGPT.Frontiers in medicine · 2026Article
- Diagnostic Performance of ChatGPT-4o in Classifying Idiopathic Epiretinal Membrane Based on Optical Coherence Tomography.Journal of clinical medicine · 2025Article
- Evaluation of the readability, quality, and accuracy of AI chatbot responses to questions about deleterious oral habits.BMC oral health · 2025Article
- To Self-Treat or Not to Self-Treat: Evaluating the Diagnostic, Advisory and Referral Effectiveness of ChatGPT Responses to the Most Common Musculoskeletal Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Large language models' capabilities in responding to tuberculosis medical questions: testing ChatGPT, Gemini, and Copilot.Scientific reports · 2025Article
- Evaluation of ChatGPT Responses About Sexual Activity After Total Hip Arthroplasty: A Comparative Study with Observers of Different Experience Levels.Journal of clinical medicine · 2025Article
- Evaluating chatbots in psychiatry: Rasch-based insights into clinical knowledge and reasoning.PloS one · 2025Article
- Evaluation of large language model-generated medical information on idiopathic pulmonary fibrosis.Frontiers in artificial intelligence · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
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
PubMed holds no abstract for this paper.
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.