Large language models improve Alzheimer's disease diagnosis using multi-modality data
This addresses the challenge of improving Alzheimer's disease diagnosis for patients by integrating non-image data, though it appears incremental as it applies an existing LLM method to a specific domain.
The paper tackled the problem of diagnosing Alzheimer's disease by leveraging multi-modality data, including non-imaging information, and achieved state-of-the-art results on the ADNI dataset.
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.