Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

干眼病 (DED) 的患病率在 5% 到 50% 之间，具体取决于所使用的诊断标准和研究人群。然而，它仍然是眼科中诊断不足和治疗不足的疾病之一。用于诊断 DED 的许多测试依赖于经验丰富的观察者进行图像解释，这可能被认为是主观的并导致诊断结果的变化。由于人工智能 (AI) 系统能够解决高级问题，因此使用此类技术可能会导致更客观的诊断。尽管“人工智能”这个词很常用，但最近它在医学上的应用取得了成功，这主要归功于机器学习子领域的进步，机器学习已被用于自动分类图像和预测医疗结果。强大的机器学习技术已被用来了解患者数据和医学图像中的细微差别，旨在实现一致的诊断和疾病严重程度的分层。这是第一篇关于在 DED 中使用 AI 的文献综述。我们简要介绍了人工智能，报告了它在 DED 研究中的当前用途及其在临床中的应用潜力。我们的审查发现，人工智能已被广泛用于 DED 临床测试和研究应用，主要用于解释干涉测量、裂隙灯和 meibography 图像。虽然初步结果很有希望，但在模型开发、临床测试和标准化方面仍需要做大量工作。我们简要介绍了人工智能，报告了它在 DED 研究中的当前用途及其在临床中的应用潜力。我们的审查发现，人工智能已被广泛用于 DED 临床测试和研究应用，主要用于解释干涉测量、裂隙灯和 meibography 图像。虽然初步结果很有希望，但在模型开发、临床测试和标准化方面仍需要做大量工作。我们简要介绍了人工智能，报告了它在 DED 研究中的当前用途及其在临床中的应用潜力。我们的审查发现，人工智能已被广泛用于 DED 临床测试和研究应用，主要用于解释干涉测量、裂隙灯和 meibography 图像。虽然初步结果很有希望，但在模型开发、临床测试和标准化方面仍需要做大量工作。