Large language models improve Alzheimer's disease diagnosis using
  multi-modality data
Yingjie Feng Jun Wang Xianfeng Gu Xiaoyin Xu Min Zhang
In diagnosing challenging conditions such as Alzheimer's disease (AD),
imaging is an important reference. Non-imaging patient data such as patient
information, genetic data, medication information, cognitive and memory tests
also play a very important role in diagnosis. Effect. However, limited by the
ability of artificial intelligence models to mine such information, most of the
existing models only use multi-modal image data, and cannot make full use of
non-image data. We use a currently very popular pre-trained large language
model (LLM) to enhance the model's ability to utilize non-image data, and
achieved SOTA results on the ADNI dataset.