ArticleOphthalmology and therapy2026
Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.
Article in Ophthalmology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis 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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
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
introductionThe aim of this study was to develop an interpretation pipeline for report generation for slit-lamp (SL) images, using vision-language models (VLMs).
methodsAn image-text alignment module (Bootstrapping Language-Image Pretraining [BLIP]) for report generation was developed using a dataset of SL images paired with medical reports from Zhejiang Provincial People's Hospital (Zhaohui Hospital). These data were split into training and internal validation sets to finetune the BLIP frameworks (LLaVA and Qwen2.5-VL). A dataset from Bijie Hospital was used as an external validation dataset for the evaluation of the frameworks.
resultsThe Zhaohui Hospital dataset included 1612 SL images and medical reports. The Bijie Hospital dataset included 100 SL images and medical reports. For the refined LLaVA framework, the BLEU scores (1-4) were 0.560-0.715, ROUGE-L score was 0.731, CIDEr score was 1.712, and SPICE score was 0.329. For the refined Qwen2.5-VL framework, the BLEU scores (1-4) were 0.536-0.696, ROUGE-L score was 0.696, CIDEr score was 1.729, and SPICE score was 0.208. Both models also showed good results in the classification of diseases. The overall accuracy was 0.87 in the refined LLaVA framework and 0.88 in the refined Qwen2.5-VL framework, with high accuracies (≥ 0.9) observed for eyelid disease, pterygium, glaucoma, corneal disease, and conjunctivitis. Interobserver agreement among ophthalmologists was substantial, with κ scores between 0.714 and 0.777. In the evaluation, the 100 reports generated by the LLaVA model demonstrated strong performance across all four metrics-correctness (2.72), completeness (2.79), harmlessness (2.88), and satisfaction (2.73)-with each scoring above 2.7. In comparison, the 100 reports produced by the Qwen model received slightly lower scores than LLaVA in correctness (2.63), completeness (2.70), and satisfaction (2.71).
conclusionsThis study introduced a framework for SL report generation, which enhanced ophthalmic image interpretation and highlighted the potential of VLMs to assist ophthalmologists and patients.
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.