Generative Artificial Intelligence for Fundus Fluorescein Angiography Interpretation and Human Expert Evaluation

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.

Contribution highlights

• This study introduces InterpreFFA, a diagnosis-supervised contrastive learning framework, to emulate ophthalmologists' decision-making process in FFA report generation.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.1038/s41746-025-01759-z

PMID

40603524

PMCID

PMC12222453

Volume

8

Issue

1

Pages / article

396

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Shao2025Generativeartificial,
  title = {Generative Artificial Intelligence for Fundus Fluorescein Angiography Interpretation and Human Expert Evaluation},
  author = {An Shao and Xiaocong Liu and Wenyue Shen and Yingyu Li and Hongkang Wu and Xiangji Pan and Zexin Yang and Yufeng Xu and Tiepei Zhu and Yao Wang and Jie Yang and Yih Chung Tham and Jian Wu and Kai Jin and Juan Ye},
  journal = {npj Digital Medicine},
  year = {2025},
  url = {https://www.nature.com/articles/s41746-025-01234-5},
  doi = {10.1038/s41746-025-01759-z},
  pmid = {40603524},
  volume = {8},
  number = {1},
  pages = {396}
}