Clinical Scorecard: A comprehensive review of transparent AI techniques for detecting cognitive decline through speech analysis

At a Glance

Key Highlights

• AI models achieve AUC values of 0.76-0.94 in detecting cognitive decline from speech features.
• Explainable AI methods such as SHAP, LIME, and attention mechanisms improve model interpretability.
• Speech biomarkers include pause patterns, speech rate, vocabulary diversity, and pronoun usage.

Guideline-Based Recommendations

Diagnosis

• Incorporate speech-based AI assessments as adjunct tools for early detection of cognitive decline.
• Use explainable AI techniques to provide transparent decision-making processes for clinicians.

Management

• Leverage AI insights to inform timely intervention and treatment planning based on speech biomarkers.

Monitoring & Follow-up

• Apply AI models with XAI to monitor progression of cognitive impairment through longitudinal speech analysis.

Risks

• Be aware of limitations due to lack of stakeholder engagement and real-world validation of AI models.
• Consider regulatory requirements such as GDPR and medical device regulations mandating AI explainability.

Patient & Prescribing Data

Patients with suspected or early cognitive impairment including Alzheimer's disease and mild cognitive impairment

Speech-based AI tools can provide personalized risk assessments highlighting specific speech features contributing to cognitive decline.

Clinical Best Practices

• Engage healthcare professionals in the development and validation of explainable AI models to enhance clinical trust.
• Use standardized evaluation frameworks to assess AI model performance and interpretability.
• Communicate AI-derived diagnostic explanations clearly to patients and caregivers to support shared decision-making.

References

• World Alzheimer Report 2019
• European Union Medical Device Regulation (MDR)
• General Data Protection Regulation (GDPR)