An End-to-End Overview of Clinical Speech AI

Area of Science:

• Biomedical Engineering
• Computational Linguistics
• Artificial Intelligence

Background:

• Speech analysis is increasingly recognized as a biomarker for various health conditions.
• Clinical speech artificial intelligence (AI) leverages supervised learning for diagnosing and monitoring neurological, mental, and motor disorders.
• Despite its potential, clinical speech AI faces challenges including data heterogeneity and sensitive protocols.

Purpose of the Study:

• To synthesize emerging literature addressing challenges in the clinical speech AI pipeline.
• To provide a practical review of technical aspects, data collection, speech representations, and modeling.
• To equip researchers and developers to translate clinical speech AI into clinical practice.

Main Methods:

• Review of emerging literature on clinical speech AI.
• Synthesis of technical pipeline components: speech elicitation, recording, representation, model development, and deployment.
• Discussion of condition-specific tasks, data collection, speech representations, predictive modeling, and ethical considerations.

Main Results:

• Identified key challenges in clinical speech AI: condition-specific tasks, data limitations, sensitive protocols, diverse representations, and label uncertainty.
• Outlined a practical technical pipeline for clinical AI.
• Presented traditional and clinically oriented speech representations and predictive modeling approaches.

Conclusions:

• Clinical speech AI holds significant promise as a scalable health assessment tool.
• Addressing technical and translational challenges is crucial for real-world clinical impact.
• Future research should focus on overcoming current limitations to advance clinical applications.