Dr AI will see you now

With the potential to change every facet of ophthalmic care, the artificial intelligence (AI) genie is out of the bottle. The FDA approved the first AI algorithm for the detection of diabetic retinopathy in 2018, and since then almost every avenue for enhanced care has been explored. For instance, AI-based models have been shown to outperform traditional formulae in intraocular lens power calculations, and more recently AI-Chatbot responses have been found to be significantly more empathetic than physician responses. Although there is little doubt the development and release of generative pre-trained transformers (GPT) has been one of the greatest advances in AI, the past decade of ophthalmic applications has been predicated on image-based analyses. One landmark application of AI assessment has been the ability to accurately determine a patient's cardiovascular risk profile from a fundus image. As part of this work, it was also clinically remarkable, that an AI network could be trained to determine biological sex using only the retinal photograph. Beyond individual patient care, AI-tools can be used for population-level profiling and discovery. For example, AI-based grading of the cup-to-disc ratio has been used to uncover novel loci associated with glaucoma. It is these image-based applications that made Ophthalmology particularly attractive in the field of AI, and despite advances in AI development, the reproducibility and broad applicability of pre-trained models remains an important area for research. In this issue, Yassin et al applied a previously developed deep convolutional network to investigate its ability to overlay multimodal images in people with Retinitis Pigmentosa. The AI network, which was based on deep learning, comprised a coarse alignment step followed by a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for retinal vessel segmentation, feature detection and description, and then outlier rejection. This AI network had been applied to a dataset containing a variety of pathologies including diabetic retinopathy, and macular degeneration. However, its performance on retinal images with more profound changes (such as Retinitis Pigmentosa) had not been determined. Yassin et al found this pre-trained AI network was significantly more accurate than manual alignment in overlaying multimodal retinal imaging. This work underscores the need to test and validate new AI models across a variety of clinical settings and applications, whilst also highlighting the fact that such validation studies can be successfully undertaken in relatively small cohorts. Some ophthalmic AI applications will have major contributions to the change in clinical care, whilst others will seem incremental and slide into clinical practise without ostentation. Our profession generally appears receptive to adopting AI as assistive tools. Image segmentation and the ability to readily overlay multimodal images from a variety of different optical instruments is one such advance, whereby it is likely to now be incorporated into the imaging suite as though this tool was always readily available.