Background Common diseases are not satisfactorily managed under the current health-care system because of inadequate medical resources and limited accessibility. We aimed to establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios, and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel health-care facilities and capture modes. The datasets were labeled using a three-step strategy: capture mode recognition (modes: mydriatic-diffuse, mydriatic-slit lamp, non-mydriatic-diffuse, and nonmydriatic-slit lamp); cataract diagnosis as a normal lens, cataract, or a postoperative eye; and detection of referable cataracts with respect to cause and severity. Area under curve [AUC] was measured at each stage. We also integrated the above cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary health care, and specialised hospital services. The diagnostic accuracy, treatment referral, and ophthalmologist-topopulation service ratio were used to evaluate the performance and efficacy of the system. Findings The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in threestep tasks: capture mode recognition (AUC 99•28-99•71% for the four different capture modes), cataract diagnosis (AUC for mydriatic-slit lamp mode 99•82% [95%CI 98•93-100] for normal lens vs 99•96% [99•90-100] for cataract vs 99•93% [99•78-100] for postoperative eye, and AUCs >99% for other capture modes), and detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30•3% of people be referred to treatment, substantially increasing the ophthalmologist-to-population service ratio by 10•2-times compared with the traditional pattern. Interpretation The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.
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