青光眼的人工智能革命:挑战与机遇并存。

IF 18.6 1区 医学 Q1 OPHTHALMOLOGY
Fei Li , Deming Wang , Zefeng Yang , Yinhang Zhang , Jiaxuan Jiang , Xiaoyi Liu , Kangjie Kong , Fengqi Zhou , Clement C. Tham , Felipe Medeiros , Ying Han , Andrzej Grzybowski , Linda M. Zangwill , Dennis S.C. Lam , Xiulan Zhang
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引用次数: 0

摘要

人工智能(AI)的最新进展预示着重塑青光眼临床管理、提高筛查效率、提高诊断精确度和完善疾病进展检测的变革潜力。然而,要将人工智能应用于医疗保健领域,在开发算法和将其付诸实践方面面临着巨大的障碍。在创建算法时,由于需要花费大量精力标注数据、诊断标准不一致以及缺乏全面测试等原因,往往会限制算法的广泛适用性。此外,人工智能算法的 "黑箱 "性质可能会引起医生的警惕或怀疑。在使用这些工具时,面临的挑战包括在真实情况下处理质量较低的图像,以及系统与不同种族群体和不同诊断设备良好协作的能力有限。展望未来,新的发展目标是通过联合学习范例保护数据隐私,通过输入数据模式的多样化提高算法的通用性,以及通过合成图像增强数据集。在临床和非临床环境中使用人工智能算法时,整合智能手机似乎大有可为。此外,引入大型语言模型(LLMs)作为医疗领域的互动工具,可能标志着未来医疗服务方式的重大变革。通过应对这些挑战并将其作为机遇加以利用,青光眼人工智能领域不仅能提高算法的准确性和优化数据整合,还能实现范式转变,提高临床接受度,实现青光眼护理的变革性改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The AI revolution in glaucoma: Bridging challenges with opportunities

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.

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来源期刊
CiteScore
34.10
自引率
5.10%
发文量
78
期刊介绍: Progress in Retinal and Eye Research is a Reviews-only journal. By invitation, leading experts write on basic and clinical aspects of the eye in a style appealing to molecular biologists, neuroscientists and physiologists, as well as to vision researchers and ophthalmologists. The journal covers all aspects of eye research, including topics pertaining to the retina and pigment epithelial layer, cornea, tears, lacrimal glands, aqueous humour, iris, ciliary body, trabeculum, lens, vitreous humour and diseases such as dry-eye, inflammation, keratoconus, corneal dystrophy, glaucoma and cataract.
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