Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning

• URL: http://arxiv.org/abs/2601.13690v1
• Date: Tue, 20 Jan 2026 07:43:57 GMT
• Title: Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning
• Authors: Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan,
• Abstract summary: Large Language Models (LLMs) have been widely adopted in healthcare due to their extensive knowledge reserves, retrieval, and communication capabilities.<n>LLMs show promise and excel at medical benchmarks, their diagnostic reasoning and inquiry skills are constrained.<n>We propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills.
• Score: 14.866862028323053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high maintenance costs and low generalization capability. Recently, Large Language Models (LLMs) have been widely adopted in healthcare due to their extensive knowledge reserves, retrieval, and communication capabilities. While LLMs show promise and excel at medical benchmarks, their diagnostic reasoning and inquiry skills are constrained. To mitigate this issue, we propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills. Its training involves a two-stage process: SFT, followed by RL with a tailored reward function. We also introduce a benchmark to evaluate both diagnostic reasoning and inquiry. Our experiments demonstrate that the Dr. Assistant outperforms open-source models and achieves competitive performance to closed-source models, providing an effective solution for clinical diagnostic inquiry guidance.

Related papers

• ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning [58.01333341218153]
  We propose ClinDEF, a dynamic framework for assessing clinical reasoning in LLMs through simulated diagnostic dialogues.<n>Our method generates patient cases and facilitates multi-turn interactions between an LLM-based doctor and an automated patient agent.<n>Experiments show that ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-12-29T12:58:58Z)
• Timely Clinical Diagnosis through Active Test Selection [49.091903570068155]
  We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design) to better emulate real-world diagnostic reasoning.<n>LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data.<n>We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use.
  arXiv  Detail & Related papers  (2025-10-21T18:10:45Z)
• Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models [51.91760712805404]
  We introduce VivaBench, a benchmark for evaluating sequential clinical reasoning in large language models (LLMs)<n>Our dataset consists of 1762 physician-curated clinical vignettes structured as interactive scenarios that simulate a (oral) examination in medical training.<n>Our analysis identified several failure modes that mirror common cognitive errors in clinical practice.
  arXiv  Detail & Related papers  (2025-10-11T16:24:35Z)
• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning [52.12425911708585]
  Deep-DxSearch is an agentic RAG system trained end-to-end with reinforcement learning (RL)<n>In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources.<n> Experiments demonstrate that our end-to-end RL training framework consistently outperforms prompt-engineering and training-free RAG approaches.
  arXiv  Detail & Related papers  (2025-08-21T17:42:47Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning [38.49879425944787]
  We propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM.<n>We train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making.<n>We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases.
  arXiv  Detail & Related papers  (2025-06-16T13:32:01Z)
• DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models [25.13622249539088]
  DiagnosisArena is a benchmark designed to rigorously assess professional-level diagnostic competence.<n> DiagnosisArena consists of 1,113 pairs of segmented patient cases and corresponding diagnoses, spanning 28 medical specialties.<n>Our study reveals that even the most advanced reasoning models, o3, o1, and DeepSeek-R1, achieve only 51.12%, 31.09%, and 17.79% accuracy, respectively.
  arXiv  Detail & Related papers  (2025-05-20T09:14:53Z)
• Expertise Is What We Want [0.0]
  We share an application architecture, the Large Language Expert (LLE), that combines the flexibility and power of Large Language Models (LLMs) with the interpretability, explainability, and reliability of Expert Systems.<n>To highlight the power of the Large Language Expert (LLE) system, we built an LLE to assist with the workup of patients newly diagnosed with cancer.
  arXiv  Detail & Related papers  (2025-02-27T18:05:15Z)
• Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning [20.369746122143063]
  We propose a dual-expertise framework that combines two complementary sources of information.<n>For external knowledge, we construct a Diagnosis Knowledge Graph (KG) that encodes both hierarchical language and semantic relations enriched by large models.<n>We introduce a lab-informed proxy task that guides the model to follow a clinically consistent stepwise reasoning process based on lab test signals.
  arXiv  Detail & Related papers  (2024-10-25T20:25:22Z)
• Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm [36.60917255464867]
  We propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novel response-proficiency-response paradigm inspired by encoder-decoder models. We show that ID-CDF can effectively address the problems without loss of diagnosis preciseness.
  arXiv  Detail & Related papers  (2023-09-01T07:18:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.