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A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection.

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Explainable AI (XAI) shows promise for detecting cognitive decline from speech, comparable to clinical tests. Further research is needed for real-world adoption and standardized evaluation of these speech analysis tools.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) models analyzing speech show potential for identifying cognitive decline, performing comparably to clinical assessments.
  • The "black box" nature of AI hinders clinical adoption due to the need for transparent decision-making processes, further complicated by regulatory requirements like GDPR and medical device regulations.
  • Explainability is crucial for healthcare professionals to trust and integrate AI tools into clinical practice for diagnosing conditions like Alzheimer's disease and mild cognitive impairment.

Purpose of the Study:

  • To systematically review explainable AI (XAI) techniques applied to speech-based detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI).
  • To assess the performance and identify key features utilized by XAI methods in speech analysis for cognitive decline detection.
  • To highlight current gaps and future directions for XAI in clinical applications for neurodegenerative diseases.

Main Methods:

  • A systematic literature review following PRISMA guidelines was conducted across six databases up to May 2025.
  • Included studies employed various XAI techniques such as SHAP, LIME, attention mechanisms, and novel approaches within machine learning architectures.
  • Analysis focused on studies utilizing speech data for the detection of cognitive decline, specifically AD and MCI.

Main Results:

  • The review identified 13 studies meeting inclusion criteria from 2077 records.
  • XAI models achieved high performance, with Area Under the Curve (AUC) values ranging from 0.76 to 0.94.
  • Consistently identified speech markers included acoustic features (pause patterns, speech rate) and linguistic features (vocabulary diversity, pronoun usage).

Conclusions:

  • XAI techniques offer promising interpretability for speech-based AI models in detecting cognitive decline.
  • Identified acoustic and linguistic features provide insights into how AI models assess cognitive status from speech.
  • Significant gaps remain in stakeholder engagement, real-world validation, and standardized evaluation frameworks for widespread clinical adoption.