SynthesisJournal of the American Medical Informatics Association : JAMIA2020
A systematic literature review of automatic Alzheimer's disease detection from speech and language.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
79 citing papers in PubMed, 2 syntheses or guidelines pooled it, 159 citations in OpenAlex.
- Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Speech-related parameters are sensitive measures of acetylcholinesterase inhibitor therapy in mild Alzheimer's disease.PloS one · 2024Trial
- Linguistic features from paragraph recall are markers of cognitive impairment.Journal of the International Neuropsychological Society : JINS · 2026Article
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Beyond Individual Features: Computational Analysis of Linguistic Coordination in Dementia Speech.Brain sciences · 2026Article
- PROTOCOL: Barriers and Facilitators to the Adoption of Speech-Based Cognitive Screening Tools: A Protocol for Systematic Review of Qualitative Evidence.Campbell systematic reviews · 2026Review
- Speech-based digital cognitive assessment for clinical trials: Detecting cognitive impairment stages and AD biomarker relations across European cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Development and validation of a machine learning model to detect psychiatric symptoms in Huntington's disease using speech analysis.PloS one · 2026Article
- Automated MoCA scoring for Arabic speakers using hybrid AI of multimodal speech, vision, and LLM integration.Frontiers in psychology · 2026Article
- Utilising speech-derived biomarkers to detect Alzheimer's disease with BERT-based language models: a machine learning study.Frontiers in artificial intelligence · 2026Article
- FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.Physical and engineering sciences in medicine · 2025Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- Article
- Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks.Journal of Alzheimer's disease : JAD · 2025Article
- Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment.Mayo Clinic proceedings. Digital health · 2025Review
- Structural Cerebral Correlates of Perplexity: Exploring a Linguistic Marker in Cognitive Aging.PsyCh journal · 2025Article
- "Is There Anything Else?": Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test.... Workshop on Cognitive Modeling and Computational Linguistics. Workshop on Cognitive Modeling and Computational Linguistics · 2025Article
- Unraveling overoptimism and publication bias in ML-driven science.Patterns (New York, N.Y.) · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. MATERIALS AND
methodsWe searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? RESULTS AND DISCUSSION: We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups.
conclusionThe systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.