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Although Alzheimer's disease (AD) cannot be reversed or cured, timelydiagnosis can significantly reduce the burden of treatment and care. Currentresearch on AD diagnosis models usually regards the diagnosis task as a typicalclassification task with two primary assumptions: 1) All target categories areknown a priori; 2) The diagnostic strategy for each patient is consistent, thatis, the number and type of model input data for each patient are the same.However, real-world clinical settings are open, with complexity and uncertaintyin terms of both subjects and the resources of the medical institutions. Thismeans that diagnostic models may encounter unseen disease categories and needto dynamically develop diagnostic strategies based on the subject's specificcircumstances and available medical resources. Thus, the AD diagnosis task istangled and coupled with the diagnosis strategy formulation. To promote theapplication of diagnostic systems in real-world clinical settings, we proposeOpenClinicalAI for direct AD diagnosis in complex and uncertain clinicalsettings. This is the first powerful end-to-end model to dynamically formulatediagnostic strategies and provide diagnostic results based on the subject'sconditions and available medical resources. OpenClinicalAI combinesreciprocally coupled deep multiaction reinforcement learning (DMARL) fordiagnostic strategy formulation and multicenter meta-learning (MCML) foropen-set recognition. The experimental results show that OpenClinicalAIachieves better performance and fewer clinical examinations than thestate-of-the-art model. Our method provides an opportunity to embed the ADdiagnostic system into the current health care system to cooperate withclinicians to improve current health care.

OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis