ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
Most LLM-driven conversational AI systems operate reactively, responding to
user prompts without guiding the interaction. Most LLM-driven conversational AI
systems operate reactively, responding to user prompts without guiding the
interaction. However, many real-world applications-such as psychiatric
diagnosis, consulting, and interviews-require AI to take a proactive role,
asking the right questions and steering conversations toward specific
objectives. Using mental health differential diagnosis as an application
context, we introduce ProAI, a goal-oriented, proactive conversational AI
framework. ProAI integrates structured knowledge-guided memory, multi-agent
proactive reasoning, and a multi-faceted evaluation strategy, enabling LLMs to
engage in clinician-style diagnostic reasoning rather than simple response
generation. Through simulated patient interactions, user experience assessment,
and professional clinical validation, we demonstrate that ProAI achieves up to
83.3% accuracy in mental disorder differential diagnosis while maintaining
professional and empathetic interaction standards. These results highlight the
potential for more reliable, adaptive, and goal-driven AI diagnostic
assistants, advancing LLMs beyond reactive dialogue systems.
