目的：住院医师委托培养是我国医学教育标准化和国际化的重要举措。本研究采用客观考核和主观问卷两种方法评估我国眼科住院医师委托培养效果。方法：本研究对象为广东省深圳市政府于

2012年8月至2015年7月期间委托中山大学中山眼科中心进行眼科住院医师规范化培训的9名学员。本研究对所有学员的基本信息、临床培训情况、以及考核成绩等客观指标进行统计分析，同时设 计了一份包含13个问题的调查问卷，对每位学员的培训情况进行主观评估。结果：本研究纳入9名研究对象，包括2名男性和7名女性，平均年龄为(26±3)岁，学历水平情况为学士7名和硕士2名，其中有7名毕业于国家重点医学院校。3年内人均轮转眼科亚专科超过10科，平均主管病人数及参加手术例数分别为736和1 219例，门诊工作总量人均6 274人次，所有学员均按规定至少完成综 述1篇。8名(88.89%)学员认为可以独立诊断并治疗大部分常见眼科疾病，而且能独立完成大部分眼科基本临床操作，5名(55.56%)学员可以单独完成翼状胬肉切除术、霰粒肿刮除术、前房穿刺术等。6名(66.67%)学员认为培训时间安排合理。8名(88.89%)学员对这次委托培养总体比较满意。 所有9名培训学员中，最终有7名(77.8%)顺利通过中山大学第一阶段住院医师规范化培训考核。结论：培训学员对现行的眼科住院医师规范化培训方案的接受程度较高，基本达到预期的培训效果，对常见眼病能独立进行诊治。委托培养学员的学历和学习能力的差异在一定程度上影响了最终的培训考核通过率。

Objective: The entrusted standardized training for residents is an important measure to gear the medical education in China to the international conventions. In this study, the effectiveness of standardized training for entrusted ophthalmic residents in China was evaluated both objectively and subjectively. Methods: Nine ophthalmic residents, commissioned by Shenzhen government of Guangdong Province, studied at Zhongshan Ophthalmic Center, Sun Yat-sen University during August 2012 to July 2015 were included in this study. The objective indicators of all participants were analyzed, including the basic information, clinical training, the score of examination, etc. The subjective self-assessment was also implemented thought a questionnaire including 13 designed questions.Results: All 9 participants included 2 males and 7 females, 2 medical masters and 7 bachelors, and the mean age was 26±3 years. Seven of them graduated from the national key medical universities. The mean number of rotated sub-clinical departments was 10.3, the mean number of managed inpatients and the participated operations were 736 and 1,219, respectively. The total number of managed outpatients was 6,274 in average. All participants completed at least one review article. Eight (88.89%) participants could independently diagnose and treat the most common ophthalmic diseases, they also could complete the basic clinical ophthalmic operation independently. Five (55.56%) participants could independently manage the pterygium excision, curettage of chalazion, anterior chamber penetration, etc. And 6 (66.67%) of the participants believed that the training length was reasonable. Eight (88.89%) of them were satisfied with the standardized training for residents on the whole. Finally, 7 participants successfully passed the first stage of standardized training program in Sun Yat-sen University. Conclusion: There was a high level acceptance rate of the standardized training programs for entrusted ophthalmic residents. The participants achieved the expected training effects, and could managed the diagnosis and treatment of common ophthalmic diseases independently. But the training effects and passing rate of examination were partly affected by the learning ability of the training students.

Background: Research innovations inoculardisease screening, diagnosis, and management have been boosted by deep learning (DL) in the last decade. To assess historical research trends and current advances, we conducted an artifcial intelligence (AI)–human hybrid analysis of publications on DL in ophthalmology.

Methods: All DL-related articles in ophthalmology, which were published between 2012 and 2022 from Web of Science, were included. 500 high-impact articles annotated with key research information were used to fne-tune alarge language models (LLM) for reviewing medical literature and extracting information. After verifying the LLM's accuracy in extracting diseases and imaging modalities, we analyzed trend of DL in ophthalmology with 2 535 articles. 

Results: Researchers using LLM for literature analysis were 70% (p= 0.000 1) faster than those who did not, while achieving comparable accuracy (97% versus 98%, p = 0.768 1). The field of DL in ophthalmology has grown 116% annually, paralleling trends of the broader DL domain. The publications focused mainly on diabetic retinopathy (p = 0.000 3), glaucoma (p = 0.001 1), and age-related macular diseases (p = 0.000 1) using retinal fundus photographs (FP, p = 0.001 5) and optical coherence tomography (OCT, p = 0.000 1). DL studies utilizing multimodal images have been growing, with FP and OCT combined being the most frequent. Among the 500 high-impact articles, laboratory studies constituted the majority at 65.3%. Notably, a discernible decline in model accuracy was observed when categorizing by study design, notwithstanding its statistical insignificance. Furthermore, 43 publicly available ocular image datasets were summarized. 

Conclusion: This study has characterized the landscape of publications on DL in ophthalmology, by identifying the trends and breakthroughs among research topics and the fast-growing areas. This study provides an efcient framework for combined AI–human analysis to comprehensively assess the current status and future trends in the feld. 

Background: Research innovations inoculardisease screening, diagnosis, and management have been boosted by deep learning (DL) in the last decade. To assess historical research trends and current advances, we conducted an artifcial intelligence (AI)–human hybrid analysis of publications on DL in ophthalmology.

Methods: All DL-related articles in ophthalmology, which were published between 2012 and 2022 from Web of Science, were included. 500 high-impact articles annotated with key research information were used to fne-tune alarge language models (LLM) for reviewing medical literature and extracting information. After verifying the LLM's accuracy in extracting diseases and imaging modalities, we analyzed trend of DL in ophthalmology with 2 535 articles. 

Results: Researchers using LLM for literature analysis were 70% (p = 0.000 1) faster than those who did not, while achieving comparable accuracy (97% versus 98%, p = 0.768 1). The field of DL in ophthalmology has grown 116% annually, paralleling trends of the broader DL domain. The publications focused mainly on diabetic retinopathy (p = 0.000 3), glaucoma (p = 0.001 1), and age-related macular diseases (p = 0.000 1) using retinal fundus photographs (FP, p = 0.001 5) and optical coherence tomography (OCT, p = 0.000 1). DL studies utilizing multimodal images have been growing, with FP and OCT combined being the most frequent. Among the 500 high-impact articles, laboratory studies constituted the majority at 65.3%. Notably, a discernible decline in model accuracy was observed when categorizing by study design, notwithstanding its statistical insignificance. Furthermore, 43 publicly available ocular image datasets were summarized. 

Conclusion: This study has characterized the landscape of publications on DL in ophthalmology, by identifying the trends and breakthroughs among research topics and the fast-growing areas. This study provides an efcient framework for combined AI–human analysis to comprehensively assess the current status and future trends in the feld. 

Artificial intelligence (AI) is about simulating and expanding human intelligence. AI based on deep learning (DL) can analyze images well by using their inherent features, such as outlines, frames and so on. As researchers generally diagnoses ocular fundus diseases by images, it makes sense to apply AI to fundus examination. In ophthalmology, AI has achieved doctor-like performance in detecting multiple ocular fundus diseases through optical coherence tomography (OCT) images, fundus photographs, and ultra-wide-field (UWF) images. It has also been widely used in disease progression prediction. Nonetheless, there are also some potential challenges with AI application in ophthalmology, one of which is the black-box problem. Researchers are devoted to developing more interpretable deep learning systems (DLS) and confirming their clinical feasibility. This review describes a summary of the state-of-the-art AI application in the most popular ocular fundus diseases, potential challenges and the path forward.

Artificial intelligence (AI) is about simulating and expanding human intelligence. AI based on deep learning (DL) can analyze images well by using their inherent features, such as outlines, frames and so on. As researchers generally diagnoses ocular fundus diseases by images, it makes sense to apply AI to fundus examination. In ophthalmology, AI has achieved doctor-like performance in detecting multiple ocular fundus diseases through optical coherence tomography (OCT) images, fundus photographs, and ultra-wide-field (UWF) images. It has also been widely used in disease progression prediction. Nonetheless, there are also some potential challenges with AI application in ophthalmology, one of which is the black-box problem. Researchers are devoted to developing more interpretable deep learning systems (DLS) and confirming their clinical feasibility. This review describes a summary of the state-of-the-art AI application in the most popular ocular fundus diseases, potential challenges and the path forward.

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.

近年来随着人类生活方式的改变、用眼频率的增加，眼科药物的市场需求持续增长，但是目前眼病治疗仍面临“缺医少药”的困境。由于新药研发面临成本高、周期长、成功率低的风险，眼科药物创新迭代的进程日趋缓慢。人工智能(artificial intelligence，AI)作为一种全新的技术手段，有望赋能眼科药物研发的全过程，包括药物靶点发现、化合物筛选、药物动力学模型创新与临床试验开展等，以期为眼科药物研发“降本增效”。且随着大数据体系的完善、硬件计算力的提升以及生命科学与智能科学的深度融合，AI在眼科药物研发中的作用将进一步得到提升，助力眼科药物研发实现从精准化到智能化的跨越。

With the change of human lifestyle and overuse of eyes in recent years, the market demand for ophthalmic drugs continues to grow. However, the ocular therapy is still facing the shortage of doctors and drugs. Due to the risk of high cost, long lead time and low success rate, the process of novel ophthalmic drug innovation and iteration is getting slower. As an emerging technology, artificial intelligence is expected to enable the whole process of ophthalmic drug discovery and development, including drug target discovery, compound screening, pharmacokinetic model innovation and clinical trials, thus reducing R&D costs and increase efficiency for ophthalmic drug discovery and development. In addition, with the improvement of big data, hardware calculation and the deep integration of life science and intelligent science, the role of artificial intelligence in ophthalmic drug discovery and development will be significant improved , contributing to achieve the leap from precision to intelligence.

手术前常规检查在临床诊疗中被广泛应用，但在一些低风险择期手术前对患者进行常规检查，对提高医疗质量并无帮助，反而降低了医疗效率，增加了医疗费用。为提高效率，一些地区、机构和专家学者陆续通过宣传教育、发表共识、制定指南等方式控制无指征术前常规检查，但效果仍依赖于执业者的重视程度和专业水平。大数据机器学习方法以其标准化、自动化的特点为解决这一问题提供了新的思路。在回顾已有研究的基础上，我们抽取2017至2019年在中山大学中山眼科中心进行眼科手术的3.4万名患者的病史和体格检查资料大数据，涵盖年龄、性别等口学信息，诊断、既往疾病等病史信息，视功能、入院时身体质量指数(BMI)等体格检查信息。并以此为基础使用机器学习方法预测术前胸部X线检查是否存在异常，受试者操作特性曲线(receiver operating characteristic curve，ROC)曲线下面积达到0.864，预测准确率可达到81.2%，对大数据机器学习精简术前常规检查的新方式进行了先期探索。

Preoperative routine tests are widely prescribed in clinical settings. However, these tests do not help improving the quality of medical care in low-risk elective surgery. Instead, they are associated with lower efficiency and increasing fees. To improve the efficiency, many regions, institutions, and scholars have attempted to reduce preoperative routine tests without indications through propaganda, education, consensus, and guidelines. Nevertheless, the effects are still highly dependent on the expertise and emphasis of practitioners. Machine learning based on big data provide a new solution with its standardization and automation. Through literature review, we extracted the big data, including demographic features such as sex and age, histories including diagnosis and chronic diseases, and physical examination features such as visual function and body mass index. A total of 34 000 patients undergone ocular surgeries in Zhongshan Ophthalmic Center, Sun Yat-sen university from 2017 to 2019. Machine learning was adopted to predict the risk of finding abnormalities in chest X-ray examination, with an accuracy of 81.2%. Area under the Receiver Operating Characteristic curve was 0.864. The study could be an early exploration into the field of simplifying preoperative tests by machine learning.

建立标准化的数据中心有利于收集高质量数据资源与促进医学人工智能的发展，在医疗大数据的基础上建立不同应用场景的医疗人工智能系统，整合、搭建可满足多种疾病诊疗需求的智能服务云平台，全面提升智能医疗管理的效率。本文以眼科为研究基础，对眼科数据中心和智能服务云平台的建设经验进行总结分析，为眼科及其他专科开展人工智能研究、建立数据中心、搭建智能服务云平台等方面提供参考。

The establishment of standardized data center can promote the accumulation of high-quality data resources and the development of medical artificial intelligence. On the basis of medical big data, medical artificial intelligence systems in different application scenarios can be established and integrated into an intelligent service cloud platform, which improves the management efficiency of intelligent medical systems. This article takes ophthalmology as a prototype to summarize the experience of the establishment of ophthalmic data center and intelligent service cloud platform, aiming to provide reference and guidance for ophthalmology and other specialties to carry out artificial intelligence research, establish data center and build an intelligent service cloud platform.

剥脱性青光眼是剥脱综合征继发的一类青光眼，临床上少见。本文报告2例患者，患眼瞳孔缘可见灰白色碎屑样物质沉积，散大瞳孔后可见晶状体前囊周边部混浊带，房角镜下可见Sampaolesi线。认识其临床特征，将有助于提高其诊治率。

Exfoliation glaucoma is a category of glaucoma secondary to exfoliation syndrome, which is rarely encountered in clinical practice. We reported 2 cases with deposits of white material on the pupillary border of the iris. Opacity band could be observed surrounding the anterior lens capsule after pupil dilation, and the Sampaolesi line was seen under gonioscope. Understanding the clinical characteristics contribute to improving the diagnosis and treatment of exfoliation glaucoma.

目的：分析医学人工智能通识课程“眼科人工智能的研发与应用”的开展效果，为相关医学人工智能通识课程的开展提供参考和借鉴。方法：纵向观察性研究。观察分析2020年秋季学期眼科人工智能的研发与应用通识课程学生人群，课程考核结果以及学生对课程的整体评价。结果：共有118名本科生同学参与了课程学习。其中大部分为低年级临床医学专业本科生。期中考核得分为77.21±10.07，有56位同学(47.46%)达到80分以上。期末考核得分为82.24±6.77，有91位同学(77.12%)达到80分以上。同学对课程的评分为98.76±3.55，超过90%的同学表示课程备课认真、授课条理清晰、表达准确。结论：本课程的顺利进展证明医学人工智能联合教学模式的可行性，理论和实践穿插的教学设置帮助同学们更好地掌握知识技术，完成教学目标。

Objective: To analyze the effectiveness of medical education curriculum named “Development and Application of Ophthalmic Artificial Intelligence”, and provide reference for the development of other related curriculums. Methods: Longitudinal observational study method was adopted. During the fall semester of 2020, we conducted an education curriculum named “Development and Application of Ophthalmic Artificial Intelligence” and analyzed the results of mid-term and final examinations, and curriculum evaluation of students. Results: There were 118 undergraduate students taking the course and most of them were junior students majoring in clinical medicine. The score of the mid-term examination was in the range of 77.2±10.07, and 56 students (47.46%) got more than 80 points. The score of the final examination was in the range of 82.24±6.77, and 91 students (77.12%) got more than 80 points. The score of course evaluation of students was in the range of 98.76±3.55, and more than 90% of the students thought that teachers have made full preparations before class, together with clear teaching logic and accurate expressions in class. Conclusion: The smooth progress of our course proved the feasibility of medical artificial intelligence teaching. The teaching setting interspersed with theory and practice could help students to master knowledge and technology better, so as to achieve the teaching objectives.