近年来，使用人工智能(artificial intelligence，AI)技术对临床大数据及图像进行分析，对疾病做出智能诊断、预测并提出诊疗决策，AI正逐步成为辅助临床及科研的先进技术。生物样本库作为收集临床信息和样本供科研使用的平台，是临床与科研的桥梁，也是临床信息与科研数据的集成平台。影响生物样本库使用效率及合理共享的因素有信息化建设水平不均衡、获取的临床及检验信息不完全、各库之间信息不对称等。本文对AI和区块链技术在生物样本库建设中的具体应用场景进行探讨，展望大数据时代智能生物样本库信息化建设的核心方向。

In recent years, artificial intelligence (AI) technology has been applied to analyze clinical big data and images and then make intelligent diagnosis, prediction and treatment decisions. It is gradually becoming an advanced technology to assist clinical and scientific research. Biobank is a platform for collecting clinical information and samples for scientific research, serving as a bridge between clinical and scientific research. It is also an integrated platform of clinical information and scientific research data. However, there are some challenges. First, clinical and laboratory information obtained is incomplete. Additionally, the information among different databases is asymmetric, which seriously impedes the information sharing among different Biobanks. In this article, the specific application scenarios of AI technology and blockchain in the construction of a Biobank were discussed, aiming to pinpoint the core direction of the information construction of an intelligent Biobank in the era of big data.

当下，我国眼科的发展存在失衡现象，大城市与农村及偏远地区在眼科相关诊疗设施水平、诊疗技术等方面存在巨大差异，仍需探寻新的智能诊疗模式以解决失衡问题。由于眼球是唯一可以直接观察人体血管和神经的器官，眼部可反映其他脏器的健康状态，部分眼科检查的医学图像可对眼部疾病做出诊断等特点，眼科开展人工智能(artificial intelligence，AI)具有独到的优势。此外，人工智能可在一定程度上提高跨时间空间传递信息的精准度及效率。人工智能在眼科及远程信息传递的优势为解决眼科发展失衡状况提供了助力。本文从眼科人工智能在眼科远程医疗相关应用的角度，主要分析并总结当下我国人工智能在眼科相关疾病远程医疗中的发展程度、所具优势以及存在问题，并讨论眼科人工智能在远程医疗的应用展望。

At present, there is an imbalance in the development of ophthalmology in China. There are huge differences in the level of ophthalmology related facilities, diagnosis and treatment technologies between big cities and rural, remote areas. New intelligent diagnosis and treatment models are still needed to solve the imbalance. Since the eye is the only organ that can directly observe the blood vessels and nerves of the human body, the eye can reflect the health status of other organs and diagnosis of eye diseases based on medical images of some ophthalmic examinations can be made as well as other characteristics. Therefore, the development of artificial intelligence in ophthalmology has unique advantages. In addition, artificial intelligence can improve the accuracy and efficiency of information transmission across time and space to a certain extent. The advantages of artificial intelligence in ophthalmology and telematics are helping to solve the imbalance in ophthalmology development. From the perspective of the application of ophthalmic artificial intelligence in telemedicine, this paper mainly analyzes and summarizes the development degree, advantages and existing problems of artificial intelligence in the telemedicine of ophthalmic diseases in China, and discusses the prospect of the application of ophthalmic artificial intelligence in telemedicine.

目的：探索和实践人工智能辅助诊断的闵行区视觉健康管理模式。方法：介绍闵行区人工智能辅助诊断的视觉健康管理模式；分析对比传统视觉健康管理模式和人工智能辅助诊断视觉健康管理模式下，社区视觉健康筛查情况，眼病发现、需转诊、复诊情况等；工作人员配置、眼科门诊接诊情况、居民眼病知识率和视觉健康服务满意情况等。结果：传统视觉健康管理模式和人工智能辅助诊断模式主要眼病（糖尿病视网膜病变、青光眼、年龄相关性黄斑变性、高度近视）发现率比较差异均有统计学意义（c²=954.03，P ＜0.01），需转诊率差异有统计学意义（c²=431.07，P ＜0.01）。人工智能辅助诊断管理模式与传统视觉健康管理模式的居民在青光眼的知晓率比较差异有统计学意义（c²=4.24，P＜0.05）。传统视觉健康管理模式和人工智能辅助诊断模式居民对视觉健康服务中的服务质量和服务时间的满意度比较差异有统计学意义（Z=-2.75，Z=-2.18，P ＜0.05）。结论：人工智能辅助诊断视觉健康管理模式，糖尿病视网膜病变、青光眼等主要眼病发现率高于传统模式，需转诊率降低，居民对青光眼的知晓率提升，在服务质量和服务时间上的居民满意度较高。基于人工智能辅助诊断的视觉健康筛查与管理模式值得本区其他社区的推广和应用。

Objective To explore and practice the visual health management mode of Minhang District . Methods Introduce the visual health management mode of AI-assisted diagnosis in Minhang District; analyze and compare the traditional visual health management mode and AI-assisted visual health management mode, community visual health screening projects and completion conditions, screening files, eye disease discovery , referral, actual referral and return ; analyze and compare the visual health management mode, staff allocation, ophthalmic outpatient reception, and the satisfaction of visual health service. Results The difference in the discovery rate of major eye diseases (Diabetic Retinopathy,Glaucoma,Age-related Macular Degeneration,High Myopia) between the traditional model and artificial intelligence-assisted diagnosis mode in both communities (c²=954.03, P<0.01), the referral rate (c²=431.07, P<0.01). The awareness of AI-assisted diagnosis management improved in glaucoma in the two modes was statistically significant (c²=4.24, P<0.05). Traditional model and artificial intelligence assisted diagnosis model of visual health service quality and service time is statistically significant (Z=-2.75, Z=-2.18, P<0.05). Conclusion The visual health screening and management mode based on AI-assisted diagnosis is worthy of the promotion and application in other communities in the region.

泪器疾病是一类常见的眼科疾病，其诊疗过程复杂，治疗方法精细，涉及多种临床数据及影像资料。现有研究表明，随着人工智能（artificial intelligence，AI）技术，尤其是机器学习和深度学习的发展，AI在泪器疾病的早期筛查、精确诊断和个性化治疗中展现了巨大的应用潜力。AI能够通过高效的图像分析、多模态数据融合及深度学习算法，提供更加精确的疾病识别和治疗方案，并且能够对患者的病情进行定期监测和动态调整，提升治疗效果。然而，其仍面临诸多挑战，如多模态数据融合的复杂性、模型泛化能力的局限以及实时预测和动态调整的需求等，需要通过持续的技术创新、算法优化和跨学科合作来实现。文章对当前AI在泪器疾病诊疗中的应用现状进行了全面梳理和总结，深入分析了AI技术在诊断与治疗中的优势与局限，特别强调了AI与新兴技术的结合在优化临床决策支持系统方面的重要性。通过分析现有的挑战与技术融合策略，文章提出了AI在泪器疾病诊疗中的发展方向，旨在为未来的研究者提供创新性的思路，为眼科领域的临床实践提供有价值的参考，助力泪器疾病的精准医疗和个性化治疗的发展。

Lacrimal disorders are common ophthalmic conditions characterized by complex diagnostic and treatment processes, involving intricate therapeutic approaches and diverse clinical and imaging data. Recent studies have indicated that with the advancements in artificial intelligence (AI) technologies, particularly in machine learning and deep learning, AI demonstrates significant potential in the early screening, accurate diagnosis, and personalized treatment of lacrimal disorders. AI has the ability to provide more precise disease identification and treatment strategies through efficient image analysis, multimodal data fusion, and deep learning algorithms. Additionally, it enables regular monitoring and dynamic adjustment of patients' conditions, improving treatment outcomes. However, several challenges persist, such as the complexity of multimodal data integration, limitations in model generalization capabilities, and the need for real-time prediction and dynamic adjustments, all of which necessitate continuous technological innovations, algorithm optimization, and interdisciplinary collaborations. This paper provides a comprehensive review of the current status of AI applications in the diagnosis and treatment of lacrimal disorders, analyzing the advantages and limitations of AI in clinical practice. It especially emphasizes the importance of integrating AI with emerging technologies to optimize clinical decision support systems. By addressing the existing challenges and exploring strategies for technological integration, this paper proposes future directions for the development of AI in lacrimal disorder diagnosis and treatment, aiming to offer innovative perspectives for future researchers and valuable references for clinical practice in the field of ophthalmology, ultimately contributing to the advancement of precision medicine and personalized treatment for lacrimal disorders.

人工智能(artificial intelligence, AI)在医学领域的广泛应用为探索眼部与全身健康的关系提供了新的机遇。文章回顾了眼科AI在心血管健康、神经系统健康、肾脏健康和衰老过程中的应用。在心血管健康方面，AI能够通过分析眼底图像预测心血管疾病风险因素和未来心血管事件，并提供了简便、有效的风险分层方法。在神经系统健康方面，眼科AI在阿尔茨海默病早期诊断和帕金森病识别方面显示出潜力，尽管对未来事件预测仍具挑战性。针对多发性硬化，眼科AI在诊断和预测残疾进程上展现了良好效果。在肾脏健康中，眼科AI技术通过分析视网膜图像可预测肾功能相关指标、直接检测肾病事件，展示了其在改善肾病筛查方式和减轻医疗负担方面的潜力。在衰老过程中，AI能够利用眼部图像预测生物年龄、视网膜年龄差和晶状体年龄等参数提供了生物衰老指标，为理解衰老与眼部健康的关联提供了新视角。

The widespread application of artificial intelligence (AI) in the medical field has provided new opportunities to explore the relationship between eye and whole body health. This article reviews the application of ophthalmic AI in cardiovascular health, neurological health and aging. In terms of cardiovascular health, AI can predict cardiovascular disease risk factors and future cardiovascular events by analyzing fundus images, and provides a simple and effective risk stratification method. In terms of neurological health, ophthalmic AI shows potential in early diagnosis of Alzheimer's disease and identification of Parkinson's disease, although the prediction of future events remains challenging. For multiple sclerosis, ophthalmic AI has shown good results in diagnosing and predicting the progression of disability. In kidney health, ophthalmic AI technology can predict kidney function-related parameters and detect kidney disease events by analyzing retinal images, demonstrating its potential in improving kidney disease screening methods and reducing medical burdens. In the aging process, AI can use eye images to predict biological age. Parameters such as retinal age gap and LensAge provide biological aging indicators, providing a new perspective for understanding the relationship between aging and eye health.

目的：借助于人工智能(artificial intelligence,AI)眼底筛查远程接转诊系统，探索“患者-社区-医院”远程筛查模式，推进眼科分级诊疗和双向转诊实施，为地市级医疗机构开展眼底疾病人工智能筛查工作提供一定的经验借鉴。方法：通过AI辅助远程筛查基层医疗机构的4886例患者，完成眼科检查并经AI初判、人工复核形成眼底诊断结论。通过医联体和专科联盟模式，对基层医疗机构的4886例患者的AI诊断系统结果和上级医师审核结果进行对照分析，分析AI诊断系统在眼科常见病种筛查中的推广应用的可信度和可行性。结果：AI检出DR的灵敏度为94.70%，特异度96.06%；DME的灵敏度96.43%，特异度96.55%；AMD的灵敏度77.55%，特异度95.74%；同时，其在病理性近视、白内障、青光眼等常见病种眼底筛查中也有一定作用。结论：AI辅助远程筛查系统对于绝大多数眼底疾病有较高的敏感性和特异性，适用于眼底疾病的筛查工作，利于基层医院或社区医院对于眼底疾病的初步诊断，落实眼科分级诊疗，有借鉴推广意义。

Objective: With the help of artificial intelligence (AI) based fundus screening remote referral telemedicine system,it enables us to explore the remote screening mode of patient-community-hospital, and promote the two-way referral and ophthalmic graded diagnosis. This investigation provides certain practice experiences for prefecture-level medical institutions to carry out AI screening for fundus diseases. Methods: Ophthalmologic examination was performed on 4,886 patients in primary medical institutions through AI-aided remote screening, and the final fundus diagnosis conclusion was formed after AI preliminary judgment and manual review. Through the Medical Consortium and specialty alliance model, the results of the AI diagnosis system and the audit results of superior physicians for 4 886 patients in primary care institutions were compared and analyzed, and the credibility and feasibility of the AI diagnosis system application in the screening of common ophthalmic diseases were discussed. Results: The sensitivity and specificity of AI detection of diabetic retinopathy were 94.70% and 96.06%, respectively. In the diabetic macular edema classification, the sensitivity and specificity were 96.43% and 96.55%, respectively. In the age-related macular degeneration classification, the sensitivity and specificity were 77.55% and 95.74%, respectively. Meanwhile, it also plays a role in screening common fundus diseases such as pathological myopia, cataract and glaucoma. Conclusion: The AI-aided remote screening system has high sensitivity and specificity for most of fundus diseases, indicating it is promising for fundus diseases screening in primary medical institutions. It is conducive for primary hospitals or community hospitals to carry out the initial diagnosis of fundus diseases, as well as the implementation of graded diagnosis and treatment of ophthalmology, which has reference and promotion significance.

目的：分析眼科护理对人工智能技术应用的内在需求，为眼科医院临床的人工智能技术开发及应用提供导向与依据。方法：采用整群抽样和单纯随机抽样相结合的方法，于2019年7月至2019年8月，对抽取的中山大学中山眼科中心，中山大学附属第一医院、珠海市人民医院、无锡人民医院、新疆维吾尔族自治区人民医院等目标医院其中的眼科护理人员进行问卷调查，内容包括一般资料及人工智能需求等。结果：调查对象绝大部分来自三级甲等医院(89.2%)，以华南地区为主(87.2%)，人工智能在眼科临床护理应用的需求多种多样，其中以健康教育、接诊与分诊、患者回访领域需求最强烈，分别占比95.7%、93.5%、93.2%。结论：人工智能在眼科临床护理应用有较强及多样化的需求，应结合实际需求为导向，重点推进人工智能在眼科患者健康教育等相关应用的研发。

Objective: To analyze the internal demands of the application of artificial intelligence technology to ophthalmic care, and provide guidance and basis for the development and application of artificial intelligence technology in ophthalmic hospitals. Methods: Using the method of combining cluster sampling with simple random sampling, a questionnaire survey was conducted on the ophthalmic nursing staff in the selected target hospitals from July to August 2019, which included general information and artificial intelligence needs. Results: Most of the respondents came from the third-class hospitals (89.2%), and hospitals in South China account for 87.2% of them. There are diverse demands of artificial intelligence in ophthalmology clinical nursing applications, including health education, clinical reception and triage, patients return visits, which have the strongest demand for the artificial intelligence, accounting for 95.7%, 93.5%, and 93.2%, respectively. Conclusion: The application of artificial intelligence in ophthalmic clinical nursing has strong and diversified demands, and the research and development of artificial intelligence in the health education of ophthalmic patients and other related applications should be promoted according to the actual demands.

近视是危害儿童青少年视力最常见的眼部疾病，高度近视对视功能造成极大的威胁。近年来，我国近视发病率逐年升高，对近视筛查与防控的需求也不断增加，随着人工智能理论与技术的不断发展与成熟，可以辅助眼科医生进行近视筛查、诊断与治疗。本文将简要介绍人工智能在近视的筛查、预测、检测、病理性近视以及角膜屈光手术中的应用，浅谈了目前人工智能在研究中存在的可比度较低、影像要求较高、可解释性较低及隐私保护等问题，并展望人工智能在近视相关领域的应用前景。

Myopia is the most common ocular disease that harms the vision of children and adolescents. High myopia poses a great threat to visual function. The incidence of myopia in China has been increasing in recent years, and the demand for myopia screening, prevention and control has also expanded. With the continuous development of artificial intelligence theory and technology, Artificial intelligence can assist ophthalmologists in myopia screening, diagnosis and treatment. This review will briefly introduce artificial intelligence in the screening, prediction, and detection of myopia; also, the application in pathological myopia and corneal refractive surgery. This review will discuss some problems of current artificial intelligence research, such as low comparability, high image requirements, low interpretability, privacy protection, and the application prospects of artificial intelligence in myopia.

目的：探索智能语音随访系统在医疗场景中的新型应用服务模式并分析其在新冠肺炎疫情期间的应用效果，以此评估该系统应用于互联网医院开展医疗咨询服务的实际效能。方法：本研究应用智能语音随访系统针对先天性白内障患儿术后的常见问题进行回访。首先，针对随访目的，设计出完善的结构化随访内容与步骤。其次，部署智能外呼系统自动拨打用户电话，并通过语音识别技术对用户的每次应答进行识别，根据用户的应答自动跳转到下一个随访步骤，在完成一系列问答后根据用户的回答给出恰当的建议，实现电话随访的自动化与智能化。收集2020年2月24日至2月28日期间，智能语音随访系统随访的电话内容、呼叫时间、患儿资料等数据，采用描述性统计分析。结果：2020年2月24日至2月28日期间，中山大学中山眼科中心应用智能语音随访系统电话共随访1154例，其中收到有效回访数据561例，平均有效回访率48.6%。有效回访人群中，有204位(36.4%)家属认为疫情期间复诊时间延长，对宝宝眼睛的恢复有影响，309位(55.1%)家属认为对宝宝眼睛的恢复没有影响。360位(64.2%)先天性白内障患儿眼睛恢复情况良好，没有出现不良反应，169位(30.1%)患儿出现不良反应和体征，包括瞳孔区有白点，眼睛发红和有眼屎流眼泪等。统计患儿不同行为显示，有417位(74.3%)患儿佩戴眼镜，135位(24.1%)患儿没有佩戴眼镜，另有9位(1.6%)患儿佩戴眼镜情况不清楚，经常揉眼的患儿更容易出现眼睛发红(20.4%)、眼睛有眼屎或流眼泪(17.0%)和瞳孔区有白点(6.8%)等不良反应。结论：智能语音随访系统在临床随访中显示出巨大的应用潜力，可作为一种新型的智能医疗服务模式。

Objective: This study was designed to explore its potential value for new medical service model based on the intelligent voice follow-up system and analyze its application effect during the outbreak of COVID-19. The actual effectiveness of this intelligent voice follow-up system applied in the Internet hospital to carry out medical consultation service was discussed. Methods: In this study, an intelligent voice follow up system was developed for postoperative follow-up of children with congenital cataract. First, a well-designed and structured questionnaire contents were developed for postoperative follow-up. Secondly, the intelligent voice follow-up system was deployed. The system would automatically jump to the next follow-up step according to the user’s response, and give appropriate suggestions. Finally, the data of telephone recording, call time, children’s attributes were collected and statistically analyzed. Results: From February 24 to March 15, 2020, 561 families of children with congenital cataract from Zhongshan Ophthalmic Center were recruited by using the intelligent voice follow-up system. The system completed a total of 1 154 calls, of which 561 cases received follow-up data, reaching an average effective call rate of 48.6%. Among 561 cases, 204 (36.4%) thought that the extended time of follow-up visit would affect the recovery of children, while 309 (55.1%) thought that it exerted no effect on the recovery. 360 children (64.2%) achieved good ocular recovery without complications, whereas 169 cases (30.1%) developed ocular symptoms. These include white spots in the pupil area, redness and eye secretions. Statistics of different behavior of children showed that there were 417 (74.3%) children wearing glasses, 135 (24.1%) children did not wear glasses, another 9 (1.6%) children wearing glasses were not clear, often rubbing the eyes of children were more likely to appear redness (20.4%), eye secretions (17.0%) and white spots in the pupil area (6.8%) and other adverse reactions. Conclusion: The intelligent voice follow-up system shows great application potential in clinical follow-up, which can be employed as a new service mode of intelligent medical treatment.

目的：评估白内障人工智能辅助诊断系统在社区筛查中的应用效果。方法：采用前瞻性观察性研究方法对白内障人工辅助诊断系统的应用效果进行分析，结合远程医疗的模式，由社区卫生人员对居民进行病史采集、视力检查和裂隙灯眼前节检查等，将数据上传至云平台，由白内障人工智能辅助诊断系统和人类医生依次进行白内障评估。结果：受检人群中男性所占比例为35.7%，年龄中位数为66岁，裂隙灯眼前节照片有98.7%的图像质量合格。该白内障人工智能辅助诊断系统在外部验证集中检出重度白内障的曲线下面积为0.915。在人类医生建议转诊的病例中，有80.3%也由人工智能系统给出了相同的建议。结论：该白内障人工智能辅助诊断系统在白内障社区筛查的应用中具有较好的可行性和准确性，为开展社区筛查疾病提供了参考依据。

Objective: To evaluate the effectiveness of an artificial intelligence-assisted diagnostic system for cataract screening in community. Methods: A prospective observational study was carried out based on a telemedicine platform. Patient history, medical records and anterior ocular segment images were collected and transmitted from community healthcare centers to Zhongshan Ophthalmic Center for evaluation by both ophthalmologists and artificial intelligence-assisted cataract diagnostic system. Results: Of all enumerated subjects, 35.7% were male and the median age was 66 years old. Of all enumerated slit-lamp images, 98.7% met the requirement of acceptable quality. This artificial intelligence-assisted diagnostic system achieved an AUC of 0.915 for detection of severe cataracts in the external validation dataset. For subjects who were advised to be referred to tertiary hospitals by doctors, 80.3% of them received the same suggestion from this artificial intelligence-assisted diagnostic system.Conclusion: This artificial intelligence-assisted cataract diagnostic system showed high applicability and accuracy in community-based cataract screening and could be a potential model of care in community-based disease screening.