# Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment

arXiv: https://arxiv.org/abs/2406.13934 · HTML: https://mlsanity.com/paper/2406.13934/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task](https://mlsanity.com/paper/2304.01097/) (2023)
- [HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs](https://mlsanity.com/paper/2311.09774/) (2023)
- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [Hierarchical Reinforcement Learning for Automatic Disease Diagnosis](https://mlsanity.com/paper/2004.14254/) (2020)

## Built on by (most cited first, 17 of 17)

- [Theory of Mind in Large Language Models: Assessment and Enhancement](https://mlsanity.com/paper/2505.00026/) (2025)
- [Advancing Conversational Diagnostic AI with Multimodal Reasoning](https://mlsanity.com/paper/2505.04653/) (2025)
- [Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning](https://mlsanity.com/paper/2510.04284/) (2025)
- [Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD](https://mlsanity.com/paper/2508.17450/) (2025)
- [Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing](https://mlsanity.com/paper/2409.11726/) (2024)
- [Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis](https://mlsanity.com/paper/2505.03467/) (2025)
- [Integrative Decoding: Improve Factuality via Implicit Self-consistency](https://mlsanity.com/paper/2410.01556/) (2024)
- [MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring](https://mlsanity.com/paper/2512.24181/) (2025)
- [Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs](https://mlsanity.com/paper/2604.08016/) (2026)
- [Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation](https://mlsanity.com/paper/2601.15161/) (2026)
- [Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction](https://mlsanity.com/paper/2506.13366/) (2025)
- [RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval](https://mlsanity.com/paper/2509.22713/) (2025)
- [A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts](https://mlsanity.com/paper/2505.03025/) (2025)
- [From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department](https://mlsanity.com/paper/2608.23627/) (2026)
- [MedThink: Enhancing Diagnostic Accuracy in Small Models via Teacher-Guided Reasoning Correction](https://mlsanity.com/paper/2605.08094/) (2026)
- [Enhancing Target-Guided Proactive Dialogue Systems via Conversational Scenario Modeling and Intent-Keyword Bridging](https://mlsanity.com/paper/2605.11964/) (2026)
- [ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls](https://mlsanity.com/paper/2608.11200/) (2026)

## Abstract (arXiv)

Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. Enabling these medical systems to emulate clinicians' diagnostic reasoning process has been the long-standing research focus. Previous studies rudimentarily realized the simulation of clinicians' diagnostic process by fine-tuning language models on high-quality dialogue datasets. Nonetheless, they overly focus on the outcomes of the clinician's reasoning process while ignoring their internal thought processes and alignment with clinician preferences. Our work aims to build a medical dialogue system that aligns with clinicians' diagnostic reasoning processes. We propose a novel framework, Emulation, designed to generate an appropriate response that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling. Experimental results on two datasets confirm the efficacy of Emulation. Crucially, our framework furnishes clear explanations for the generated responses, enhancing its transparency in medical consultations.