# Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review

arXiv: https://arxiv.org/abs/2010.06047 · HTML: https://mlsanity.com/paper/2010.06047/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge](https://mlsanity.com/paper/2301.05562/) (2023)
- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [Connected Speech-Based Cognitive Assessment in Chinese and English](https://mlsanity.com/paper/2406.10272/) (2024)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (2022)
- [Learning De-identified Representations of Prosody from Raw Audio](https://mlsanity.com/paper/2107.08248/) (2021)
- [Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment](https://mlsanity.com/paper/2501.18731/) (2025)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Unraveling overoptimism and publication bias in ML-driven science](https://mlsanity.com/paper/2405.14422/) (2024)
- [DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech](https://mlsanity.com/paper/2410.04188/) (2024)
- [Zero-Shot Cognitive Impairment Detection from Speech Using AudioLLM](https://mlsanity.com/paper/2506.17351/) (2025)
- [Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech](https://mlsanity.com/paper/2602.14655/) (2026)
- [Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?](https://mlsanity.com/paper/2203.17110/) (2022)
- [Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum](https://mlsanity.com/paper/2502.18285/) (2025)
- [Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's](https://mlsanity.com/paper/2209.06085/) (2022)
- [Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model](https://mlsanity.com/paper/2402.01824/) (2024)
- [Dementia classification from spontaneous speech using wrapper-based feature selection](https://mlsanity.com/paper/2502.03484/) (2025)
- [CAtCh: Cognitive Assessment through Cookie Thief](https://mlsanity.com/paper/2506.06603/) (2025)
- [ClaritySpeech: Dementia Obfuscation in Speech](https://mlsanity.com/paper/2507.09282/) (2025)
- [PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring](https://mlsanity.com/paper/2511.16445/) (2025)
- [Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study](https://mlsanity.com/paper/2512.20948/) (2025)
- [What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection](https://mlsanity.com/paper/2602.11177/) (2026)
- [Multilingual Cognitive Impairment Detection in the Era of Foundation Models](https://mlsanity.com/paper/2604.06758/) (2026)
- [Predicting Psychological Well-Being from Spontaneous Speech using LLMs](https://mlsanity.com/paper/2605.11303/) (2026)
- [Fair Cognitive Impairment Detection Through Unlearning](https://mlsanity.com/paper/2606.18571/) (2026)
- [Transcript-Free Lightweight Detection of Alzheimer's Disease from Spontaneous Speech Using Handcrafted MFCC-Dominant Acoustic Biomarkers](https://mlsanity.com/paper/2607.10168/) (2026)
- [Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models](https://mlsanity.com/paper/2607.21496/) (2026)
- [Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection](https://mlsanity.com/paper/2608.25028/) (2026)
- [Why Alzheimer's Speech Screening Fails to Generalize: Bridging the Deployment Gap via Cross-Corpus Evidence Anchoring](https://mlsanity.com/paper/2609.31293/) (2026)

## Abstract (arXiv)

Language is a valuable source of clinical information in Alzheimer's Disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. This paper summarises current findings on the use of artificial intelligence, speech and language processing to predict cognitive decline in the context of Alzheimer's Disease, detailing current research procedures, highlighting their limitations and suggesting strategies to address them. We conducted a systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase) and Web of Science. Bibliographies of relevant papers were screened until December 2019. From 3,654 search results 51 articles were selected against the eligibility criteria. Four tables summarise their findings: study details (aim, population, interventions, comparisons, methods and outcomes), data details (size, type, modalities, annotation, balance, availability and language of study), methodology (pre-processing, feature generation, machine learning, evaluation and results) and clinical applicability (research implications, clinical potential, risk of bias and strengths/limitations). While promising results are reported across nearly all 51 studies, very few have been implemented in clinical research or practice. We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Attempts to close these gaps should support translation of future research into clinical practice.