# Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators

arXiv: https://arxiv.org/abs/2501.09484 · HTML: https://mlsanity.com/paper/2501.09484/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://mlsanity.com/paper/2406.00922/) (2024)
- [ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences](https://mlsanity.com/paper/2311.06025/) (2023)
- [Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://mlsanity.com/paper/2408.15787/) (2024)
- [A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?](https://mlsanity.com/paper/2409.15277/) (2024)
- [Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA](https://mlsanity.com/paper/2003.03875/) (2020)

## Built on by (most cited first, 8 of 8)

- [Baichuan-M2: Scaling Medical Capability with Large Verifier System](https://mlsanity.com/paper/2509.02208/) (2025)
- [The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability](https://mlsanity.com/paper/2509.24958/) (2025)
- [Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations](https://mlsanity.com/paper/2505.20201/) (2025)
- [Efficient Medical VIE via Reinforcement Learning](https://mlsanity.com/paper/2506.13363/) (2025)
- [METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues](https://mlsanity.com/paper/2604.11427/) (2026)
- [Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond](https://mlsanity.com/paper/2502.20988/) (2025)
- [ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning](https://mlsanity.com/paper/2512.23440/) (2025)
- [Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs](https://mlsanity.com/paper/2602.01995/) (2026)

## Abstract (arXiv)

Recently, large language models have shown great potential to transform online medical consultation. Despite this, most research targets improving diagnostic accuracy with ample information, often overlooking the inquiry phase. Some studies try to evaluate or refine doctor models by using prompt-engineered patient agents. However, prompt engineering alone falls short in accurately simulating real patients. We need to explore new paradigms for patient simulation. Furthermore, the relationship between inquiry and diagnosis remains unexplored. This paper extracts dialogue strategies from real doctor-patient conversations to guide the training of a patient simulator. Our simulator shows higher anthropomorphism and lower hallucination rates, using dynamic dialogue strategies. This innovation offers a more accurate evaluation of diagnostic models and generates realistic synthetic data. We conduct extensive experiments on the relationship between inquiry and diagnosis, showing they adhere to Liebig's law: poor inquiry limits diagnosis effectiveness, regardless of diagnostic skill, and vice versa. The experiments also reveal substantial differences in inquiry performance among models. To delve into this phenomenon, the inquiry process is categorized into four distinct types. Analyzing the distribution of inquiries across these types helps explain the performance differences. The weights of our patient simulator are available https://github.com/PatientSimulator/PatientSimulator.