ArticleNPJ digital medicine2025
Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 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
12 citing papers 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
- Intelligent Staging Performance of Diabetic Retinopathy Based on Fundus Fluorescein Angiography Images with Different Angiographic Phases.Bioengineering (Basel, Switzerland) · 2026Article
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Fundus to fluorescein angiography video generation as a retinal generative foundation model.NPJ digital medicine · 2026Article
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.Communications medicine · 2025Article
- Leveraging ChatGPT for Report Error Audit: An Accuracy-Driven and Cost-Efficient Solution for Ophthalmic Imaging Reports.Ophthalmology and therapy · 2025Article
- Knowledge-enhanced AI drives diagnosis of multiple retinal diseases in fundus fluorescein angiography.Frontiers in cell and developmental biology · 2025Article
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but its interpretation requires significant expertise and time. Despite generative AI's enormous potential in medical report generation, automatic FFA interpretation lacks robust models and sufficient evaluation metrics. This study introduces InterpreFFA, a diagnosis-supervised contrastive learning framework, to emulate ophthalmologists' decision-making process in FFA report generation. Validated on multi-center datasets, InterpreFFA demonstrated superior performance and generalization compared to baseline models. In a simulated clinical setting, two residents used InterpreFFA to diagnose and report FFA cases, with six board-certified ophthalmologists rating the generated reports based on a five-point Likert scale. InterpreFFA significantly improved diagnostic accuracy (85.55 to 90.34%, p < 0.05) and shortened reporting time (153.93 to 108.08 s, p < 0.001). Although AI-generated reports scored slightly lower than manual reports (4.12 vs. 4.38, p < 0.01), InterpreFFA proves to be a promising and cost-effective ancillary tool for enhancing clinical efficiency.
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.