The close link between cognitive decline and language has fostered long-standing collaboration between the NLP and medical communities in dementia research. To examine this, we reviewed over 200 papers applying NLP to dementia related efforts, drawing from medical, technological, and NLP-focused literature. We identify key research areas, including dementia detection, linguistic biomarker extraction, caregiver support, and patient assistance, showing that half of all papers focus solely on dementia detection using clinical data. However, many directions remain unexplored: artificially degraded language models, synthetic data, digital twins, and more. We highlight gaps and opportunities around trust, scientific rigor, applicability, and cross-community collaboration, and showcase the diverse datasets encountered throughout our review: recorded, written, structured, spontaneous, synthetic, clinical, social media based, and more. This review aims to inspire more creative approaches to dementia research within the medical and NLP communities.

本研究探讨了自然语言处理（NLP）与痴呆研究之间的紧密联系，回顾了200多篇相关文献，识别了关键研究领域，如痴呆检测和语言生物标志物提取。研究发现，尽管已有大量研究集中在临床数据的痴呆检测上，但仍存在许多未被探索的方向，为医药与NLP领域的跨界合作提供了新的机会。

自然语言处理在痴呆研究中的系统综述：任务、数据集与机遇