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.

慢性病尚待解决的一个挑战是如何将患者和医疗资源从长期监测和定期就诊的负担中解放出来。基于人工智能（AI）的精准管理大有可为；然而，完全集成预测和远程医疗计算的临床应用尚未实现，需要进一步努力来验证其在现实世界中的益处。以先天性白内障为代表，我们利用贝叶斯和深度学习算法，打造了集个性化预测和调度、智能远程医疗随访计算于一体的AI智能体CC-Guardian。我们的代理在内部和多资源验证中表现出高灵敏度和特异性。我们将代理与基于网络的智能手机应用程序集成，并构建预测远程医疗云平台原型以支持我们的智能随访系统。然后，我们进行了回顾性自我对照测试，验证我们的系统不仅可以准确地检测和解决早期阶段的并发症，而且与传统方法相比还可以减轻社会经济负担。这项研究代表了应用人工智能实现真正医疗效益的开创性一步，并展示了一种有效管理慢性病的新策略。