D oc CHA : Towards LLM -Augmented Interactive Online diagnosis System (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% higher diagnostic accuracy and over 30% improvement in symptom recall, with only modest increase in dialogue turns. These results demonstrate DocCHA’s effectiveness in enabling structured, transparent, and efficient diagnostic conversations—paving the way for trustworthy LLM-powered clinical assistants in multilingual and resource-constrained settings.

## introduction large language models (llms) have dramatically enhanced the capabilities of conversational agents across domains such as education @xcite , climate policy @xcite , and healthcare @xcite . in the medical domain, llm-based conversational health agents (chas) have been deployed for tasks including health report generation @xcite , patient education @xcite , and clinical information extraction @xcite . existing approaches typically fall into three paradigms: (i) zero-shot or few-shot prompting of general-purpose llms @xcite , (ii) fine-tuned domain-specific models @xcite , and (iii) multimodal extensions incorporating imaging or physiological signals @xcite . despite recent advances, most current chas lack the core reasoning behaviors expected in realworld clinical workflows. they rely on static prompts or scripted flows that fail to account for ambiguous, incomplete, or evolving user input. in contrast, clinical diagnosis is a highly iterative process-clinicians adaptively identify missing information, elicit symptom nuances (e.g., severity, progression), integrate patient history, and reason causally across heterogeneous data sources @xcite . these dynamic and contingent behaviors are not well captured by existing llm-based chas, leading to oversimplified questioning and unreliable diagnostic decisions. to address this gap, we introduce doccha, a modular, confidence-aware framework designed to emulate human-like diagnostic reasoning in multiturn dialogue settings. as shown in figure 1 , doc-cha decomposes the diagnostic process into three core stages-symptom elicitation, history acquisition, and causal reasoning-each powered by dedicated modules that operate under confidence-and quota-based control. this modularity enables doc-cha to adaptively probe information gaps, prioritize high-yield clarifications, and construct interpretable causal graphs that justify diagnostic conclusions. extensive experiments on two chinese diagnostic dialogue datasets-imcs21 and dx-demonstrate that doccha significantly improves performance over prompting-only baselines. specifically, it achieves up to +5.18% improvement in accuracy over gpt-4o and up to +29.63% over llama-3, while extracting over 30% more medically relevant information under the same turn budget. these results validate that structure-aware, confidence-guided reasoning offers distinct advantages beyond llm scale. ## method we propose doccha, a modular, confidencedriven diagnostic framework that emulates multiturn clinical reasoning with llms. the architecture consists of three sequential modules: (1) symptom collection, (2) history acquisition, and (3) causal graph construction and refinement. each module integrates a dedicated confidence function to evaluate information sufficiency and trigger follow-up questions. these metrics are explicitly designed to maximize diagnostic discriminability @xcite , reduce uncertainty @xcite , and support structured causal reasoning @xcite . ## experiments we evaluate doccha on two key capabilities essential for interactive medical diagnosis: (1) accurately predicting final diagnoses through multi-turn patient interaction, and (2) eliciting clinically critical information with the coverage and precision of real doctors. experiments are conducted on two real-world doctor-patient dialogue datasets, using matched interaction budgets and comparisons with strong prompting baselines. ## conclusion we introduced doccha, a confidence-aware, modular diagnostic agent that emulates real-world clinical reasoning through structured multi-turn dialogue. by integrating discriminative symptom collection, multi-dimensional history acquisition, and causal graph-based reasoning, doccha significantly improves diagnostic accuracy and information recall over strong llm baselines-while maintaining interaction efficiency. our experiments and sensitivity analysis validate the robustness and interpretability of this design, showcasing its potential for building reliable and adaptive llmpowered health agents. this framework also lays a foundation for broader deployment in low-resource or multilingual clinical settings, where trust, transparency, and diagnostic completeness are essential.