Although Alzheimer's disease (AD) cannot be reversed or cured, timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task with two primary assumptions: 1) All target categories are known a priori; 2) The diagnostic strategy for each patient is consistent, that is, the number and type of model input data for each patient are the same. However, real-world clinical settings are open, with complexity and uncertainty in terms of both subjects and the resources of the medical institutions. This means that diagnostic models may encounter unseen disease categories and need to dynamically develop diagnostic strategies based on the subject's specific circumstances and available medical resources. Thus, the AD diagnosis task is tangled and coupled with the diagnosis strategy formulation. To promote the application of diagnostic systems in real-world clinical settings, we propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings. This is the first powerful end-to-end model to dynamically formulate diagnostic strategies and provide diagnostic results based on the subject's conditions and available medical resources. OpenClinicalAI combines reciprocally coupled deep multiaction reinforcement learning (DMARL) for diagnostic strategy formulation and multicenter meta-learning (MCML) for open-set recognition. The experimental results show that OpenClinicalAI achieves better performance and fewer clinical examinations than the state-of-the-art model. Our method provides an opportunity to embed the AD diagnostic system into the current health care system to cooperate with clinicians to improve current health care.

翻译：尽管阿尔茨海默病（AD）无法逆转或治愈，但及时诊断可显著减轻治疗与护理负担。现有AD诊断模型研究通常将诊断任务视为典型分类任务，并基于两个基本假设：1）所有目标类别先验已知；2）每位患者的诊断策略一致，即模型输入数据的数量与类型对每位患者相同。然而，真实临床环境具有开放性，在受试者及医疗机构资源方面均存在复杂性与不确定性。这意味着诊断模型可能遭遇未见疾病类别，且需根据受试者具体状况与可用医疗资源动态制定诊断策略。因此，AD诊断任务与诊断策略制定相互交织耦合。为促进诊断系统在真实临床场景中的应用，我们提出OpenClinicalAI，用于在复杂不确定的临床环境中直接进行AD诊断。这是首个强大的端到端模型，能根据受试者状况与可用医疗资源动态制定诊断策略并给出诊断结果。OpenClinicalAI将互耦深度多动作强化学习（DMARL）与多中心元学习（MCML）分别用于诊断策略制定与开放集识别。实验结果表明，相比现有最优模型，OpenClinicalAI实现了更优性能并减少了临床检查次数。本方法为将AD诊断系统嵌入当前医疗体系以协助临床医师改善医疗保健提供了可行途径。