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眼科公共卫生中的人工智能技术:当前应用与未来方向。

Artificial intelligence technology in ophthalmology public health: current applications and future directions.

作者信息

Chen ShuYuan, Bai Wen

机构信息

Xuzhou Medical University, Xuzhou, China.

The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.

出版信息

Front Cell Dev Biol. 2025 Apr 17;13:1576465. doi: 10.3389/fcell.2025.1576465. eCollection 2025.

Abstract

Global eye health has become a critical public health challenge, with the prevalence of blindness and visual impairment expected to rise significantly in the coming decades. Traditional ophthalmic public health systems face numerous obstacles, including the uneven distribution of medical resources, insufficient training for primary healthcare workers, and limited public awareness of eye health. Addressing these challenges requires urgent, innovative solutions. Artificial intelligence (AI) has demonstrated substantial potential in enhancing ophthalmic public health across various domains. AI offers significant improvements in ophthalmic data management, disease screening and monitoring, risk prediction and early warning systems, medical resource allocation, and health education and patient management. These advancements substantially improve the quality and efficiency of healthcare, particularly in preventing and treating prevalent eye conditions such as cataracts, diabetic retinopathy, glaucoma, and myopia. Additionally, telemedicine and mobile applications have expanded access to healthcare services and enhanced the capabilities of primary healthcare providers. However, there are challenges in integrating AI into ophthalmic public health. Key issues include interoperability with electronic health records (EHR), data security and privacy, data quality and bias, algorithm transparency, and ethical and regulatory frameworks. Heterogeneous data formats and the lack of standardized metadata hinder seamless integration, while privacy risks necessitate advanced techniques such as anonymization. Data biases, stemming from racial or geographic disparities, and the "black box" nature of AI models, limit reliability and clinical trust. Ethical issues, such as ensuring accountability for AI-driven decisions and balancing innovation with patient safety, further complicate implementation. The future of ophthalmic public health lies in overcoming these barriers to fully harness the potential of AI, ensuring that advancements in technology translate into tangible benefits for patients worldwide.

摘要

全球眼健康已成为一项严峻的公共卫生挑战,预计在未来几十年中,失明和视力障碍的患病率将大幅上升。传统的眼科公共卫生系统面临众多障碍,包括医疗资源分配不均、基层医疗工作者培训不足以及公众对眼健康的认知有限。应对这些挑战需要迫切的创新解决方案。人工智能(AI)已在提升眼科公共卫生的各个领域展现出巨大潜力。AI在眼科数据管理、疾病筛查与监测、风险预测与预警系统、医疗资源分配以及健康教育与患者管理方面有显著改善。这些进步极大地提高了医疗保健的质量和效率,特别是在预防和治疗白内障、糖尿病视网膜病变、青光眼和近视等常见眼病方面。此外,远程医疗和移动应用扩大了医疗服务的可及性,并增强了基层医疗服务提供者的能力。然而,将AI整合到眼科公共卫生中存在挑战。关键问题包括与电子健康记录(EHR)的互操作性、数据安全与隐私、数据质量与偏差、算法透明度以及伦理和监管框架。异构数据格式和缺乏标准化元数据阻碍了无缝集成,而隐私风险需要诸如匿名化等先进技术。源于种族或地域差异的数据偏差以及AI模型的“黑箱”性质限制了可靠性和临床信任。伦理问题,如确保对AI驱动的决策负责以及在创新与患者安全之间取得平衡,使实施进一步复杂化。眼科公共卫生的未来在于克服这些障碍,以充分发挥AI的潜力,确保技术进步转化为全球患者的切实利益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0ac/12044197/53ae0b23ade8/fcell-13-1576465-g001.jpg

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