Artificial intelligence in ophthalmology: The path to the real-world clinic

Abstract

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.

Keywords: artificial intelligence; clinical translation; deep learning; eye diseases; machine learning; ophthalmology; real world.

Graphical abstract

Figure 1

Relationship between AI, machine learning, and deep learning SVM, support vector machine. PCA, principal-component analysis.

Figure 2

Overall schematic diagram describing the practical application of AI in all common ophthalmic imaging modalities

Figure 3

Medical AI translational challenges between system development and routine clinical application There are five major challenges in the path of AI clinical translation: validity, generalizability, interpretability, longevity, and liability.

Figure 4

Framework of federated learning Local hospitals are given a copy of a current global model from a federated server to train on their own datasets. After a certain number of iterations, the local hospitals send model updates back to the federated server and keep their datasets in their own secure infrastructure. The federated server aggerates the contributions from these hospitals. Then the updated global model is shared with the local hospitals and they can continue local training. The main advantage of federated learning is that it establishes a global model without directly sharing datasets, preserving patient privacy across sites.