ArticleNPJ digital medicine2025
A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Why health recommender systems struggle to reach clinical practice: A lifecycle-oriented systematic review.iScience · 2026Article
- Evaluating Cognition Across Aging and Traumatic Brain Injury: Integrating Neurological and Neuropsychological Approaches.Journal of clinical medicine · 2026Review
- A Speech Analytics-Based Methodological Protocol for Monitoring Orthopedic Rehabilitation in the Brazilian Unified Health System.International journal of environmental research and public health · 2026Article
- Benchmarking speech biomarkers of Alzheimer's against cognitive and neural measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Can speech-based AI transform cognitive impairment screening?Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice.Frontiers in aging neuroscience · 2026Review
- Automated MoCA scoring for Arabic speakers using hybrid AI of multimodal speech, vision, and LLM integration.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence models analyzing speech show remarkable promise for identifying cognitive decline, achieving performance comparable to clinical assessments. However, their "black box" nature poses significant barriers to clinical adoption, as healthcare professionals require transparent decision-making processes. This challenge is compounded by regulatory requirements, including GDPR mandates for explainability and medical device regulations emphasizing AI transparency. Following PRISMA guidelines, we systematically reviewed explainable AI (XAI) techniques for speech-based detection of Alzheimer's disease and mild cognitive impairment across six databases through May 2025. From 2077 records, 13 studies met the inclusion criteria, employing XAI methods including SHAP, LIME, attention mechanisms, and novel approaches across machine learning architectures. Models achieved AUC values of 0.76-0.94, consistently identifying acoustic markers (pause patterns, speech rate) and linguistic features (vocabulary diversity, pronoun usage). While XAI techniques demonstrate promise for clinical interpretability, significant gaps remain in stakeholder engagement, real-world validation, and standardized evaluation frameworks.
Identifiers
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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.