ArticleJournal of medical Internet research2024
Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Application of deep learning strategies in the standardization and diagnostic efficiency enhancement of chest X-ray imaging.BMC medical imaging · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- ChatMyopia: An AI agent for myopia-related consultation in primary eye care settings.iScience · 2025Article
- A multimodal visual-language foundation model for computational ophthalmology.NPJ digital medicine · 2025Article
- A deep learning-driven cataract screening model derived from multicenter real-world dataset.Frontiers in medicine · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
- Benchmarking large multimodal models for ophthalmic visual question answering with OphthalWeChat.Advances in ophthalmology practice and researchArticle
- DeepSeek-R1 outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in bilingual complex ophthalmology reasoning.Advances in ophthalmology practice and researchArticle
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9 authors.
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Abstract
backgroundLarge language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
objectiveWe aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2. We term this entire process slit lamp-GPT.
methodsOur research used a dataset of 25,051 slit lamp images from 3409 participants, paired with their corresponding physician-created medical reports. We used these data, split into training, validation, and test sets, to finetune the Bootstrapping Language-Image Pre-training framework toward report generation. The generated text reports and human-posed questions were then input into Llama2 for subsequent QA. We evaluated performance using qualitative metrics (including BLEU [bilingual evaluation understudy], CIDEr [consensus-based image description evaluation], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence], SPICE [Semantic Propositional Image Caption Evaluation], accuracy, sensitivity, specificity, precision, and F
resultsWe identified 50 conditions related to diseases or postoperative complications through keyword matching in initial reports. The refined slit lamp-GPT model demonstrated BLEU scores (1-4) of 0.67, 0.66, 0.65, and 0.65, respectively, with a CIDEr score of 3.24, a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score of 0.61, and a Semantic Propositional Image Caption Evaluation score of 0.37. The most frequently identified conditions were cataracts (22.95%), age-related cataracts (22.03%), and conjunctival concretion (13.13%). Disease classification metrics demonstrated an overall accuracy of 0.82 and an F
conclusionsThis study introduces the slit lamp-GPT model for report generation and subsequent QA, highlighting the potential of large language models to assist ophthalmologists and patients.
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