Comprehensive Summary
Xu et al. studied the baseline methods for automatic report generation via the use of fundus fluorescein angiography (FFA) and indocyanine green angiography (ICGA). The research was performed by Xu et al. via the annotation of 58,520 FFA images, through which a classification-based and language-based baseline method was developed and evaluated via metrics such as the F1 score and BERTScore. The findings from these tests concluded that the general efficacy of the classification-based method had high success in recognition of choroidal neovascularization (CNV), whilst the language-based model had a higher efficacy in identification of hyperfluorescence types and impression recognition. These results are crucial as they demonstrate how both baseline methods for FFA and ICGA report generation are very high-performing in these tasks, and are thus valuable for developing multimodal AI research.
Outcomes and Implications
The research performed by Xu et al. is important in the fact that it highlights the potential for advancing large language models due to the positive results from the classification-based and language-based methodology. Specifically, this research applies clinically due to the extreme lack of AI models used for FFA and ICGA report generation. In fact, Xu et al. proclaim that there are no AI models that can achieve this level of accuracy in clinical practice as of now. As such, though this research only helps introduce and set the stage for future development of AI regarding FFA and ICGA report analyses, it is a step forward that can potentially help create a new generation of clinical practice.