Large Language Models Improve Alzheimer's Disease Diagnosis Using Multi-Modality Data
Yingjie Feng, Xiaoyin Xu, Yueting Zhuang, Min Zhang
Abstract
In diagnosing challenging conditions such as Alzheimer's disease (AD), while imaging is an important reference, non-image data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use image data, and cannot make comprehensive use of non-image data. We based our approach on an existing pretrained large language model (LLM) to enhance the model's ability to utilize non-image data on diagnosing AD. Our test results on the ADNI dataset show that the approached achieved state-of-the-art (SOTA) performance.