Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technology, called OCT angiography (OCTA), capable of visualizing retina vasculature. However, due to its cost and limited view, OCTA technology is not widely used. Retinal fundus photography is a safe imaging technique used for capturing the overall structure of the retina. In order to visualize retinal vasculature without the need for FA and in a cost-effective, non-invasive, and accurate manner, we propose a deep learning conditional generative adversarial network (GAN) capable of producing FA images from fundus photographs. The proposed GAN produces anatomically accurate angiograms, with similar fidelity to FA images, and significantly outperforms two other state-of-the-art generative algorithms ( $$p<.001$$p < . 001 and $$p<.0001$$p < . 0001 ). Furthermore, evaluations by experts shows that our proposed model produces such high quality FA images that are indistinguishable from real angiograms. Our model as the first application of artificial intelligence and deep learning to medical image translation, by employing a theoretical framework capable of establishing a shared feature-space between two domains (i.e. funduscopy and fluorescein angiography) provides an unrivaled way for the translation of images from one domain to the other.

Bard AI, developed by Google, represents a significant advancement in the realm of artificial intelligence (AI) powered chatbots [1]. This innovative AI is designed to simulate human-like interactions, answering a wide array of user inquiries and prompts [1]. Having been trained on a vast dataset of text data, Bard utilizes sophisticated language models to generate comprehensive and informative responses to user inputs [1]. The mechanism behind Bard involves its large language model, specifically the Pathways Language Model 2 (PaLM 2), which has been engineered to excel in comprehending facts, logical reasoning, and mathematics [1]. The capacity of the model to effectively analyze and comprehend contextual information enables it to deliver comprehensive and precise responses to a wide array of inquiries [1, 2]. While Bard shares some similarities with ChatGPT, Bard AI distinguishes itself through its emphasis on factual accuracy. Bard demonstrates exceptional proficiency in understanding and answering questions that require precise information [1]. In contrast, ChatGPT, a generative language model, is proficient in generating creative and diverse types of text, such as articles, code, scripts, and more which may or may not be completely accurate [3]. One of the fundamental differences between Bard AI and ChatGPT lies in their data sources and connections to the internet. Bard AI is linked to Google's expansive search network, granting it access to real-time information from the internet [1]. This allows Bard AI to provide up-to-date information about current events and topics [1]. On the other hand, ChatGPT is not directly connected to the internet, and leverages data up to 2021 to generate its responses [3, 4]. ChatGPT has already shown considerable potential in the field of ophthalmology, from helping triage ophthalmic symptoms [5], medical education [6], answering ophthalmic board certification practice questions [7], writing cataract surgery operative notes [8], developing ophthalmology AI algorithms to classify OCT images [9]. The utilization of Bard AI has considerable potential upside and downside for ophthalmology. The ability of Bard AI to comprehend and provide factual information makes it a valuable tool for