DocCHA: Towards LLM-Augmented Interactive Online diagnosis System
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Despite the impressive capabilities of Large Language Models (LLMs), existing
Conversational Health Agents (CHAs) remain static and brittle, incapable of
adaptive multi-turn reasoning, symptom clarification, or transparent
decision-making. This hinders their real-world applicability in clinical
diagnosis, where iterative and structured dialogue is essential. We propose
DocCHA, a confidence-aware, modular framework that emulates clinical reasoning
by decomposing the diagnostic process into three stages: (1) symptom
elicitation, (2) history acquisition, and (3) causal graph construction. Each
module uses interpretable confidence scores to guide adaptive questioning,
prioritize informative clarifications, and refine weak reasoning links.
Evaluated on two real-world Chinese consultation datasets (IMCS21, DX),
DocCHA consistently outperforms strong prompting-based LLM baselines (GPT-3.5,
GPT-4o, LLaMA-3), achieving up to 5.18 percent higher diagnostic accuracy and
over 30 percent improvement in symptom recall, with only modest increase in
dialogue turns. These results demonstrate the effectiveness of DocCHA in
enabling structured, transparent, and efficient diagnostic conversations --
paving the way for trustworthy LLM-powered clinical assistants in multilingual
and resource-constrained settings.
