In NPJ digital medicine
A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.
Long Erping, Chen Jingjing, Wu Xiaohang, Liu Zhenzhen, Wang Liming, Jiang Jiewei, Li Wangting, Zhu Yi, Chen Chuan, Lin Zhuoling, Li Jing, Li Xiaoyan, Chen Hui, Guo Chong, Zhao Lanqin, Nie Daoyao, Liu Xinhua, Liu Xin, Dong Zhe, Yun Bo, Wei Wenbin, Xu Fan, Lv Jian, Li Min, Ling Shiqi, Zhong Lei, Chen Junhong, Zheng Qishan, Zhang Li, Xiang Yi, Tan Gang, Huang Kai, Xiang Yifan, Lin Duoru, Zhang Xulin, Dongye Meimei, Wang Dongni, Chen Weirong, Liu Xiyang, Lin Haotian, Liu Yizhi
Computer science, Health care economics, Lens diseases, Translational research