• Ying-Chun Jheng, Yu-Bai Chou, Chung-Lan Kao, Aliaksandr A Yarmishyn, Chih-Chien Hsu, Tai-Chi Lin, Po-Yin Chen, Zih-Kai Kao, Shih-Jen Chen, and De-Kuang Hwang.
• Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
• J Chin Med Assoc. 2020 Oct 1; 83 (10): 898-899.

AbstractArtificial intelligence (AI) has been widely applied in the medical field and achieved enormous milestones in helping specialists to make diagnosis and remedy decisions, particularly in the field of eye diseases and ophthalmic screening. With the development of AI-based systems, the enormous hardware and software resources are required for optimal performance. In reality, there are many places on the planet where such resources are highly limited. Hence, the smartphone-based AI systems can be used to provide a remote control route to quickly screen eye diseases such as diabetic-related retinopathy or diabetic macular edema. However, the performance of such mobile-based AI systems is still uncharted territory. In this article, we discuss the issues of computing resource consumption and performance of the mobile device-based AI systems and highlight recent research on the feasibility and future potential of application of the mobile device-based AI systems in telemedicine.

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