ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
An explainable machine learning model of cognitive decline derived from speech.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Speech and language outcome measures in clinical trials of Alzheimer's and Parkinson's diseases.Expert review of neurotherapeutics · 2026Review
- Evaluating spoken language as a biomarker for automated screening of cognitive impairment.Communications medicine · 2025Article
- Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.MethodsX · 2025Article
- A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection.NPJ digital medicine · 2025Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.medRxiv : the preprint server for health sciences · 2025Article
- Navigating the tradeoff between personal privacy and data utility in speech anonymization for clinical research.NPJ digital medicine · 2025Review
- Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning.The Journal of nutrition · 2025Article
- A hybrid approach for pattern recognition and interpretation in age-related false memory.Frontiers in psychology · 2025Article
- Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis.Sleep science and practice · 2025Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
- An explainable machine learning model of cognitive decline derived from speech.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
introductionTraditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders.
methodsSpeech collected over the telephone from 91 older participants who were cognitively healthy (
resultsThis approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data. DISCUSSION: This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies. Highlights: Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
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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.