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本文引用的文献

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Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.在基层医疗诊所中用于检测糖尿病视网膜病变的基于人工智能的自主诊断系统的关键试验。
NPJ Digit Med. 2018 Aug 28;1:39. doi: 10.1038/s41746-018-0040-6. eCollection 2018.
2
Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.用于检测早产儿严重视网膜病变的深度学习图像评估系统的评估
Br J Ophthalmol. 2018 Nov 23. doi: 10.1136/bjophthalmol-2018-313156.
3
An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs.基于彩色眼底照片的威胁视力可转诊糖尿病视网膜病变自动分级系统。
Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1.
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Clinically applicable deep learning for diagnosis and referral in retinal disease.临床适用的深度学习在视网膜疾病的诊断和转诊中的应用。
Nat Med. 2018 Sep;24(9):1342-1350. doi: 10.1038/s41591-018-0107-6. Epub 2018 Aug 13.
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Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography.基于光学相干断层扫描的糖尿病视网膜病变自动诊断与分级。
Invest Ophthalmol Vis Sci. 2018 Jun 1;59(7):3155-3160. doi: 10.1167/iovs.17-23677.
6
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.使用深度卷积神经网络自动诊断早产儿视网膜病变中的 Plus 病。
JAMA Ophthalmol. 2018 Jul 1;136(7):803-810. doi: 10.1001/jamaophthalmol.2018.1934.
7
A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.一种基于深度学习的算法,可从眼底彩色照相图预测年龄相关性眼病研究严重程度评分-年龄相关性黄斑变性。
Ophthalmology. 2018 Sep;125(9):1410-1420. doi: 10.1016/j.ophtha.2018.02.037. Epub 2018 Apr 10.
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Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.基于眼底彩色照片的深度学习系统检测青光眼视神经病变的效果。
Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.
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Clinical Applicability of Deep Learning System in Detecting Tuberculosis with Chest Radiography.深度学习系统在胸部X光检测肺结核中的临床适用性
Radiology. 2018 Feb;286(2):729-731. doi: 10.1148/radiol.2017172407.
10
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.使用来自多民族糖尿病患者群体的视网膜图像开发并验证用于糖尿病视网膜病变及相关眼病的深度学习系统
JAMA. 2017 Dec 12;318(22):2211-2223. doi: 10.1001/jama.2017.18152.

人工智能与眼科学。

Artificial Intelligence and Ophthalmology.

机构信息

Bahçeşehir University Faculty of Medicine, Department of Ophthalmology, Division of Medical Ethics and History of Medicine, İstanbul, Turkey

Health Sciences University Bakırköy Training and Research Hospital, Clinic of Ophthalmology, İstanbul, Turkey

出版信息

Turk J Ophthalmol. 2020 Mar 5;50(1):37-43. doi: 10.4274/tjo.galenos.2020.78989.

DOI:10.4274/tjo.galenos.2020.78989
PMID:32167262
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7086098/
Abstract

Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic of medical ethics. Various artificial intelligence applications related to the diagnosis of eye diseases were researched in books, journals, search engines, print and social media. Resources were cross-checked to verify the information. Artificial intelligence algorithms, some of which were approved by the US Food and Drug Administration, have been adopted in the field of ophthalmology, especially in diagnostic studies. Studies are being conducted that prove that artificial intelligence algorithms can be used in the field of ophthalmology, especially in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Some of these algorithms have come to the approval stage. The current point in artificial intelligence studies shows that this technology has advanced considerably and shows promise for future work. It is believed that artificial intelligence applications will be effective in identifying patients with preventable vision loss and directing them to physicians, especially in developing countries where there are fewer trained professionals and physicians are difficult to reach. When we consider the possibility that some future artificial intelligence systems may be candidates for moral/ethical status, certain ethical issues arise. Questions about moral/ethical status are important in some areas of applied ethics. Although it is accepted that current intelligence systems do not have moral/ethical status, it has yet to be determined what the exact the characteristics that confer moral/ethical status are or will be.

摘要

人工智能正在迅速发展,并渗透到我们生活的各个领域。本综述讨论了人工智能在眼科领域的应用以及相关的医学伦理问题的发展和潜在实践。在书籍、期刊、搜索引擎、印刷品和社交媒体中研究了与眼部疾病诊断相关的各种人工智能应用。对资源进行了交叉检查以验证信息。一些已获得美国食品和药物管理局批准的人工智能算法已在眼科领域得到应用,尤其是在诊断研究中。正在进行的研究证明,人工智能算法可用于眼科领域,特别是在糖尿病视网膜病变、年龄相关性黄斑变性和早产儿视网膜病变中。其中一些算法已经进入批准阶段。人工智能研究的当前阶段表明,这项技术已经取得了相当大的进展,为未来的工作带来了希望。人们相信,人工智能应用将有效地识别出可预防视力丧失的患者,并将其引导至医生处,尤其是在专业人员较少且医生难以接触的发展中国家。当我们考虑到某些未来的人工智能系统可能具有道德/伦理地位的可能性时,就会出现某些伦理问题。道德/伦理地位的问题在某些应用伦理学领域很重要。尽管人们普遍认为当前的智能系统没有道德/伦理地位,但尚未确定赋予道德/伦理地位的确切特征是什么,或者将来会是什么。