Abstract：
OBJECTIVE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline.
METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline.
RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%).
CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.
METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline.
RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%).
CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.
摘要：
目的：在眼科实践中，使用电子健康记录（EHR）收集的数据量迅速增加。人工智能（AI）提供了一种集中数据收集和分析的有前途的手段，但迄今为止,大多数人工智能算法仅应用于眼科实践中的图像数据分析。在这篇综述中，我们旨在描述人工智能在EHR分析中的应用，并严格评估每个纳入研究对CONSORT-AI报告指南的依从性。
方法：对三个相关数据库（MEDLINE，EMBASE,和Cochrane图书馆）于2010年1月至2023年2月进行。根据CONSORT-AI报告指南中的AI特定项目，对纳入研究的报告质量进行了评估。
结果：在我们搜索的4,968篇文章中，89项研究符合所有纳入标准，被纳入本综述。大多数研究利用人工智能进行眼部疾病预测(n=41，46.1%)，糖尿病性视网膜病变是研究最多的眼部病理（n=19，21.3％）。14个测量项目的总体平均CONSORT-AI评分为12.1（范围8-14，中位数12）。依从率最低的类别是：描述处理质量差的数据(48.3%)，指定参与者纳入和排除标准(56.2%)，并详细说明对AI干预或其代码的访问，包括任何限制（62.9%）。
结论：结论：我们已经发现人工智能在眼科诊所中被显著地用于疾病预测，然而，这些算法由于缺乏通用性和跨中心可重复性而受到限制。应制定AI报告的标准化框架，改善人工智能在眼科疾病管理和眼科决策中的应用。
方法：对三个相关数据库（MEDLINE，EMBASE,和Cochrane图书馆）于2010年1月至2023年2月进行。根据CONSORT-AI报告指南中的AI特定项目，对纳入研究的报告质量进行了评估。
结果：在我们搜索的4,968篇文章中，89项研究符合所有纳入标准，被纳入本综述。大多数研究利用人工智能进行眼部疾病预测(n=41，46.1%)，糖尿病性视网膜病变是研究最多的眼部病理（n=19，21.3％）。14个测量项目的总体平均CONSORT-AI评分为12.1（范围8-14，中位数12）。依从率最低的类别是：描述处理质量差的数据(48.3%)，指定参与者纳入和排除标准(56.2%)，并详细说明对AI干预或其代码的访问，包括任何限制（62.9%）。
结论：结论：我们已经发现人工智能在眼科诊所中被显著地用于疾病预测，然而，这些算法由于缺乏通用性和跨中心可重复性而受到限制。应制定AI报告的标准化框架，改善人工智能在眼科疾病管理和眼科决策中的应用。
