Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the “black box” nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

中文翻译：

---

青光眼的 AI 革命：用机遇应对挑战


人工智能 （AI） 的最新进展预示着重塑青光眼临床管理、提高筛查效果、提高诊断精度和完善疾病进展检测的变革潜力。然而，将 AI 纳入医疗保健用途在开发算法并将其付诸实践方面面临重大障碍。在创建算法时，由于标记数据所需的大量工作、不一致的诊断标准以及缺乏彻底的测试，问题会出现，这通常限制了算法的广泛适用性。此外，AI 算法的“黑匣子”性质可能会导致医生保持警惕或怀疑。在使用这些工具时，挑战包括在真实情况下处理低质量的图像，以及系统与不同种族群体和不同诊断设备良好配合的能力有限。展望未来，新的发展旨在通过联合学习范式保护数据隐私，通过多样化输入数据模态来提高算法泛化性，并使用合成图像增强数据集。智能手机的集成似乎很有希望在临床和非临床环境中使用 AI 算法。此外，引入大型语言模型 （LLMs） 作为医学中的交互式工具可能意味着未来医疗保健提供方式的重大变化。通过应对这些挑战并利用这些作为机遇，青光眼 AI 领域不仅将提高算法准确性和优化数据集成，而且还将实现向增强临床接受度和青光眼护理变革性改进的范式转变。