AI-Enhanced Fundus Fluorescein Angiography: A Medical Breakthrough

Generative artificial intelligence (AI) is making significant strides in medical imaging, particularly in the interpretation of fundus fluorescein angiography (FFA). FFA, a gold standard for visualizing retinal vasculature, has been crucial in diagnosing various chorioretinal diseases for over three decades. However, the complexity and time-consuming nature of FFA interpretation have long posed challenges, especially in areas with a scarcity of experienced ophthalmologists.

Generative AI and large language models (LLMs) are transforming the landscape of medical image interpretation. These advanced techniques can generate new content, including text, images, and video, based on learned patterns from existing data. Recent advancements have shown promising results in assisting physicians with interpreting different medical images, enhancing human-in-the-loop clinical decision-making, and supporting disease diagnosis and prognosis prediction.

Studies have demonstrated that generative AI methods can produce chest radiograph reports of similar quality to those of radiologists, effectively shortening the reporting time and improving reader performance and efficiency. These outcomes highlight the potential of generative AI to streamline diagnostic workflows and deliver timely and accurate patient care.

Despite these advantages, the development of AI-aided FFA interpretation systems faces several challenges. The complexity of FFA images and the lack of open-source datasets with corresponding natural language reports limit research on automatic FFA interpretation. Additionally, existing studies often focus on improving the literal similarity between generated reports and real-world reports, rather than clinical value. Conventional natural language generation (NLG) metrics do not reflect the improvements in clinical settings.

To address these issues, researchers have invited senior ophthalmologists to evaluate the accuracy of generated reports based on medical questions. Some studies have extracted keywords from original FFA reports as classification labels to assess the accuracy, partially reflecting the clinical efficacy of generated reports. However, automatic report generation is currently accepted only for ancillary use, not for producing instant reports for patients.

To further promote the adoption of AI-aided systems in FFA interpretation, it is crucial to validate the clinical efficacy of generated reports and the impact of these systems on the report-writing process. The InterpreFFA system, a diagnosis-supervised contrastive learning framework, aims to enhance the quality of ophthalmic reporting and support clinical decision-making. By comprehensively studying the impact of InterpreFFA, researchers hope to validate it as a promising and cost-effective tool for improving ophthalmic care.

In summary, generative AI is poised to revolutionize the interpretation of FFA, addressing the challenges of complexity and scarcity of skilled ophthalmologists. As research continues to validate the clinical efficacy of these systems, the future of ophthalmic care looks increasingly promising.