ArticleiScience2024
ChatFFA: An ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Review
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ChatMyopia: An AI agent for myopia-related consultation in primary eye care settings.iScience · 2025Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation.Ophthalmology and therapy · 2025Review
- Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration.Journal of medical Internet research · 2024Article
- EyeGPT for Patient Inquiries and Medical Education: Development and Validation of an Ophthalmology Large Language Model.Journal of medical Internet research · 2024Article
- 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
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing automatic analysis of fundus fluorescein angiography (FFA) images faces limitations, including a predetermined set of possible image classifications and being confined to text-based question-answering (QA) approaches. This study aims to address these limitations by developing an end-to-end unified model that utilizes synthetic data to train a visual question-answering model for FFA images. To achieve this, we employed ChatGPT to generate 4,110,581 QA pairs for a large FFA dataset, which encompassed a total of 654,343 FFA images from 9,392 participants. We then fine-tuned the Bootstrapping Language-Image Pre-training (BLIP) framework to enable simultaneous handling of vision and language. The performance of the fine-tuned model (ChatFFA) was thoroughly evaluated through automated and manual assessments, as well as case studies based on an external validation set, demonstrating satisfactory results. In conclusion, our ChatFFA system paves the way for improved efficiency and feasibility in medical imaging analysis by leveraging generative large language models.
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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.