ArticleDigital health
Comparison of three large language models in recognizing ophthalmological examination and supporting preoperative toric IOL planning.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Comparative performance of contemporary multimodal large language models in retinal imaging question answering.Frontiers in medicine · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the performance of three large language models (LLMs) in automated recognition of IOLMaster 700 reports and preoperative toric intraocular lens (IOL) planning. Methods: The retrospective study analyzed preoperative examination reports of patients who underwent cataract surgery with toric IOL implantation. Three models (ChatGPT-5, ChatGPT-5 Thinking and DeepSeek Thinking) were instructed to extract key biometric parameters, evaluate a patient's suitability for toric IOL implantation, and generate a plan. Model performance was evaluated based on structured-data recognition, refractive prediction outcomes and thinking times. Results: Fifty-four eyes of 54 patients were analyzed. ChatGPT-5 Thinking model consistently achieved the highest agreement with clinical reference for all extracted parameters, and demonstrated more reliable extraction of axis information. ChatGPT-5 showed intermediate performance, while DeepSeek Thinking was the least consistent in axis-dependent fields but performed adequately for basic biometry. Refractive and axis prediction errors were smallest with ChatGPT-5 Thinking, yielding the largest proportion of cases within prespecified clinical thresholds and the highest concordance with the calculator-based reference plan. Analysis of thinking times showed that longer processing did not necessarily correlate with better accuracy. Conclusions: Advanced LLMs show promise for automated interpretation of ophthalmic biometry reports and calculator-based toric IOL planning workflows. These findings support the feasibility of LLM-assisted workflow automation, with ChatGPT-5 Thinking providing the most favorable balance of accuracy and efficiency in this setting.
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.