A Systematic Review of NLP for Dementia: Tasks, Datasets, and Opportunities

Abstract

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 240 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. Yet, many directions remain unexplored, such as artificially degraded language models, synthetic data, digital twins, and more. We highlight gaps and opportunities around trust, scientific rigor, applicability, and cross-community collaboration. We raise ethical dilemmas in the field, and highlight the diverse datasets encountered throughout our review including recorded, written, structured, spontaneous, synthetic, clinical, social media–based, and more. This review aims to inspire more creative, impactful, and rigorous research on NLP for dementia.