A Pilot Study on Clinician-AI Collaboration in Diagnosing Depression from Speech

TL;DR

This pilot study explores clinicians' perceptions of a speech-based explainable AI system for depression detection, highlighting its potential benefits and challenges in clinical integration.

Contribution

It introduces a speech-based, explainable AI system for depression diagnosis and evaluates clinicians' perceptions through a small user study.

Findings

Providing more explanations increases trust but also complexity.

Clinicians see potential for integration into workflows.

Limitations include clinicians' AI familiarity and visualization needs.

Abstract

This study investigates clinicians' perceptions and attitudes toward an assistive artificial intelligence (AI) system that employs a speech-based explainable ML algorithm for detecting depression. The AI system detects depression from vowel-based spectrotemporal variations of speech and generates explanations through explainable AI (XAI) methods. It further provides decisions and explanations at various temporal granularities, including utterance groups, individual utterances, and within each utterance. A small-scale user study was conducted to evaluate users' perceived usability of the system, trust in the system, and perceptions of design factors associated with several elements of the system. Quantitative and qualitative analysis of the collected data indicates both positive and negative aspects that influence clinicians' perception toward the AI. Results from quantitative analysis indicate that providing more AI explanations enhances user trust but also increases system complexity. Qualitative analysis indicates the potential of integrating such systems into the current diagnostic and screening workflow, but also highlights existing limitations including clinicians' reduced familiarity with AI/ML systems and the need for user-friendly and intuitive visualizations of speech information.