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Review
. 2024 Jul-Aug;13(4):100082.
doi: 10.1016/j.apjo.2024.100082. Epub 2024 Jul 15.

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives

William Rojas-Carabali  1 , Carlos Cifuentes-González  2 , Laura Gutierrez-Sinisterra  3 , Lim Yuan Heng  4 , Edmund Tsui  5 , Sapna Gangaputra  6 , Srinivas Sadda  7 , Quan Dong Nguyen  8 , John H Kempen  9 , Carlos E Pavesio  10 , Vishali Gupta  11 , Rajiv Raman  12 , Chunyan Miao  13 , Bernett Lee  14 , Alejandra de-la-Torre  15 , Rupesh Agrawal  16
Affiliations
Free article
Review

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives

William Rojas-Carabali et al. Asia Pac J Ophthalmol (Phila). 2024 Jul-Aug.
Free article

Abstract

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health services. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease etiologies, analyze multimodal imaging data, predict outcomes, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.

Keywords: Applications; Artificial Intelligence; Diagnosis; Uveitis.

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