Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore.
Department of Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore.
Asia Pac J Ophthalmol (Phila). 2024 Jul-Aug;13(4):100082. doi: 10.1016/j.apjo.2024.100082. Epub 2024 Jul 15.
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.
人工智能（AI）与医疗保健的融合为诊断、治疗和管理医疗状况开辟了新途径，具有极高的精准度。葡萄膜炎是一组罕见的眼部疾病，其特征是葡萄膜炎症，由于其病因、临床表现和治疗反应的多样性，在眼科学中极具复杂性。如果葡萄膜炎不能及时有效地治疗，可能会导致严重的视力损害。然而，其管理需要专业知识，而在医疗服务有限的地区，这种知识往往是缺乏的。AI 在模式识别、数据分析和预测建模方面的能力具有极大的潜力，可以彻底改变葡萄膜炎的管理方式。AI 可以对疾病病因进行分类，分析多模态成像数据，预测结果，并识别新的治疗靶点。然而，要将这些 AI 模型转化为临床应用并满足患者的期望，需要克服一些挑战，如获取广泛的、有注释的数据集，确保算法的透明度，并在实际环境中验证这些模型。本综述深入探讨了葡萄膜炎的复杂性和当前的 AI 领域，讨论了 AI 从理论模型到床边应用的开发、机遇和挑战。它还研究了葡萄膜炎的流行病学、全球葡萄膜炎专家短缺以及该疾病的社会经济影响，强调了 AI 驱动方法的重要性。此外，它还探讨了 AI 在诊断成像中的整合以及眼科的未来方向，旨在突出可能改变葡萄膜炎患者管理的新兴趋势，并建议加强 AI 在临床实践中的应用的合作努力。
