Tabuchi Hitoshi
Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima, Japan.
Department of Ophthalmology, Saneikai Tsukazaki Hospital, Himeji City, Hyogo Prefecture, Japan.
Taiwan J Ophthalmol. 2022 Apr 13;12(2):123-129. doi: 10.4103/tjo.tjo_8_22. eCollection 2022 Apr-Jun.
Applications of artificial intelligence technology, especially deep learning, in ophthalmology research have started with the diagnosis of diabetic retinopathy and have now expanded to all areas of ophthalmology, mainly in the identification of fundus diseases such as glaucoma and age-related macular degeneration. In addition to fundus photography, optical coherence tomography is often used as an imaging device. In addition to simple binary classification, region identification (segmentation model) is used as an identification method for interpretability. Furthermore, there have been AI applications in the area of regression estimation, which is different from diagnostic identification. While expectations for deep learning AI are rising, regulatory agencies have begun issuing guidance on the medical applications of AI. The reason behind this trend is that there are a number of existing issues regarding the application of AI that need to be considered, including, but not limited to, the handling of personal information by large technology companies, the black-box issue, the flaming issue, the theory of responsibility, and issues related to improving the performance of commercially available AI. Furthermore, researchers have reported that there are a plethora of issues that simply cannot be solved by the high performance of artificial intelligence models, such as educating users and securing the communication environment, which are just a few of the necessary steps toward the actual implementation process of an AI society. Multifaceted perspectives and efforts are needed to create better ophthalmology care through AI.
人工智能技术，尤其是深度学习，在眼科研究中的应用始于糖尿病视网膜病变的诊断，如今已扩展到眼科的所有领域，主要用于青光眼和年龄相关性黄斑变性等眼底疾病的识别。除眼底摄影外，光学相干断层扫描常被用作成像设备。除简单的二元分类外，区域识别（分割模型）被用作一种具有可解释性的识别方法。此外，在回归估计领域也有人工智能的应用，这与诊断识别有所不同。虽然对深度学习人工智能的期望不断提高，但监管机构已开始发布关于人工智能医疗应用的指导意见。这一趋势背后的原因是，人工智能应用存在一些现有问题需要考虑，包括但不限于大型科技公司对个人信息的处理、黑箱问题、炒作问题、责任理论以及与提高商用人工智能性能相关的问题。此外，研究人员报告称，人工智能模型的高性能根本无法解决大量问题，比如对用户进行培训以及确保通信环境安全，而这些只是迈向人工智能社会实际实施过程的几个必要步骤。需要多方面的视角和努力，通过人工智能创造更好的眼科护理。
