ArticleBMC ophthalmology2025
Zero-shot LLM-based visual acuity extraction: a pilot study.
Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Pneumatic retinopexy identification in electronic medical records using an on-premises large language model.Eye (London, England) · 2026Article
- How Often Do Large Language Models Agree with Each Other-And with the Truth? A Consensus- and Complexity-Stratified Analysis of Data Extraction for Neuroimaging AI.Journal of clinical medicine · 2026Article
- Evaluating Large Language Models for Automated Evidence Synthesis in Neuroimaging AI: A Multi-Model Benchmark.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
introductionVisual acuity (VA) and intraocular pressure (IOP) measurements are crucial indicators of ocular health. However, the documentation of these vital parameters in clinical notes often lacks standardisation. The aim of this study was to evaluate the feasibility of using of large language models (LLMs) for automated extraction of VA and IOP data from unstructured ophthalmology clinic notes.
methodOutpatient clinic notes from The Queen Elizabeth Hospital Ophthalmology department were analysed using a 70 billion parameter LLaMA-3 model in a zero-shot learning approach. Nine data points per eye were extracted, including various VA measurements, IOP, and measurement methods. Results were compared to expert-verified ground truth data.
resultsSixty-nine outpatient clinic notes were collected. Locally deployed LLM analysis of clinic notes was feasible. High accuracy was observed in extracting best corrected VA and IOP (above 90% for both eyes). Performance varied for other measurements, with lower accuracy for uncorrected VA (67-75%) and challenges in interpreting ambiguous documentation. The model struggled to identify assumed uncorrected VA cases, highlighting issues which may correlate with documentation clarity.
conclusionThis study has demonstrated that it is feasible to deploy a LLM locally to extract VA data from free-text notes. However, the accuracy of extracted data was lacking in several domains. These performance issues highlight that further research is required to optimise the accuracy of the retrieved data.
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.