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

TL;DR

OpenClinicalAI is a novel end-to-end model that dynamically formulates diagnostic strategies for Alzheimer's disease in complex clinical settings, incorporating open-set recognition and reinforcement learning to improve accuracy and reduce examinations.

Contribution

It introduces the first model capable of adapting diagnostic strategies based on patient conditions and medical resources, addressing real-world clinical complexities.

Findings

Achieves better performance than state-of-the-art models.

Requires fewer clinical examinations.

Effectively recognizes unseen disease categories.

Abstract

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.