Conversational Disease Diagnosis via External Planner-Controlled Large
  Language Models
Zhoujian Sun Cheng Luo Zhengxing Huang
The advancement of medical artificial intelligence (AI) has set the stage for
the realization of conversational diagnosis, where AI systems mimic human
doctors by engaging in dialogue with patients to deduce diagnoses. This study
introduces an innovative approach using external planners augmented with large
language models (LLMs) to develop a medical task-oriented dialogue system. This
system comprises a policy module for information gathering, a LLM based module
for natural language understanding and generation, addressing the limitations
of previous AI systems in these areas. By emulating the two-phase
decision-making process of doctors disease screening and differential
diagnosis. we designed two distinct planners. The first focuses on collecting
patient symptoms to identify potential diseases, while the second delves into
specific inquiries to confirm or exclude these diseases. Utilizing
reinforcement learning and active learning with LLMs, we trained these planners
to navigate medical dialogues effectively. Our evaluation on the MIMIC-IV
dataset demonstrated the system's capability to outperform existing models,
indicating a significant step towards achieving automated conversational
disease diagnostics and enhancing the precision and accessibility of medical
diagnoses.