Najafi Alireza, Babaei Samane, Sadoughi Mohammad Mehdi, Kalantarion Masomeh, Sadatmoosavi Ali
Department of Medical Education, School of Medical Education and Learning Technologies, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Ophthalmic Vis Res. 2025 Jul 30;20. doi: 10.18502/jovr.v20.17029. eCollection 2025.
This systematic review investigated the role of artificial intelligence (AI) in the knowledge, attitude, and performance of ophthalmology residents. We conducted a comprehensive systematic search in international databases including PubMed, Web of Science, Scopus, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Education Resources Information Center (ERIC) using keywords "artificial intelligence", "deep learning", "ophthalmology", "ocular surgery", and "education" and their synonyms. The keywords were extracted from medical research studies published from January 1, 2018 to April 15, 2024. The quality of these studies was evaluated by using the STORBE, JADA, and JBI appraisal tools. Six studies were selected based on the defined criteria. Specifically, five of these studies investigated the effectiveness of AI interventions on the performance of ophthalmology residents in diagnosing myopia, corneal diseases (using a confocal microscope), staging of diabetic retinopathy, abnormal findings in posterior segment ultrasonography, including retinal detachment, posterior vitreous detachment, and vitreous hemorrhage, and 13 fundus diseases. One study investigated the residents' attitudes about the application of an AI model for providing feedback in cataract surgery. All six studies showed positive results. Due to the small number of studies found through our systematic search and the variations in the investigated outcomes and study settings, it was not possible to conduct a meta-analysis. Despite the positive reports on improving the diagnostic performance of residents and their attitude toward the usability of AI models in cataract surgery, it is recommended that more studies be conducted in this area. These studies should replicate previous investigations using similar study settings while maintaining high quality standards and addressing existing limitations.
本系统评价研究了人工智能（AI）在眼科住院医师的知识、态度和表现方面所起的作用。我们在国际数据库中进行了全面的系统检索，这些数据库包括PubMed、科学网、Scopus、护理学与健康相关文献累积索引（CINAHL）、教育资源信息中心（ERIC），使用的关键词为“人工智能”“深度学习”“眼科”“眼科手术”和“教育”及其同义词。这些关键词是从2018年1月1日至2024年4月15日发表的医学研究中提取的。采用STORBE、JADA和JBI评估工具对这些研究的质量进行了评估。根据既定标准选择了六项研究。具体而言，其中五项研究调查了人工智能干预对眼科住院医师在诊断近视、角膜疾病（使用共聚焦显微镜）、糖尿病视网膜病变分期、后段超声检查异常发现（包括视网膜脱离、玻璃体后脱离和玻璃体积血）以及13种眼底疾病方面表现的有效性。一项研究调查了住院医师对应用人工智能模型在白内障手术中提供反馈的态度。所有六项研究均显示出积极结果。由于通过我们的系统检索发现的研究数量较少，且所调查的结果和研究环境存在差异，因此无法进行荟萃分析。尽管有关于提高住院医师诊断表现以及他们对白内障手术中人工智能模型可用性的态度的积极报告，但建议在该领域进行更多研究。这些研究应在保持高质量标准并解决现有局限性的同时，使用类似的研究环境重复先前的调查。
