ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning
- URL: http://arxiv.org/abs/2512.23440v1
- Date: Mon, 29 Dec 2025 12:58:58 GMT
- Title: ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning
- Authors: Yuqi Tang, Jing Yu, Zichang Su, Kehua Feng, Zhihui Zhu, Libin Wang, Lei Liang, Qiang Zhang, Keyan Ding, Huajun Chen,
- Abstract summary: 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.
- Score: 58.01333341218153
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Clinical diagnosis begins with doctor-patient interaction, during which physicians iteratively gather information, determine examination and refine differential diagnosis through patients' response. This dynamic clinical-reasoning process is poorly represented by existing LLM benchmarks that focus on static question-answering. To mitigate these gaps, recent methods explore dynamic medical frameworks involving interactive clinical dialogues. Although effective, they often rely on limited, contamination-prone datasets and lack granular, multi-level evaluation. In this work, we propose ClinDEF, a dynamic framework for assessing clinical reasoning in LLMs through simulated diagnostic dialogues. Grounded in a disease knowledge graph, our method dynamically generates patient cases and facilitates multi-turn interactions between an LLM-based doctor and an automated patient agent. Our evaluation protocol goes beyond diagnostic accuracy by incorporating fine-grained efficiency analysis and rubric-based assessment of diagnostic quality. Experiments show that ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs, offering a more nuanced and clinically meaningful evaluation paradigm.
Related papers
- AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
arXiv Detail & Related papers (2026-01-23T11:59:13Z) - AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Grounding [4.87216588304398]
We introduce AutoMedic, a multi-agent simulation framework that enables automated evaluation of large language models (LLMs) as clinical conversational agents.<n>AutoMedic transforms off-the-shelf static QA datasets into virtual patient profiles, enabling realistic and clinically grounded multi-turn clinical dialogues.<n>The performance of various clinical conversational agents is then assessed based on our CARE metric, which provides a multi-faceted evaluation standard of clinical conversational accuracy, efficiency/strategy, empathy, and robustness.
arXiv Detail & Related papers (2025-12-11T01:25:36Z) - 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) - 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) - CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making [16.310913127940857]
We introduce CliBench, a novel benchmark developed from the MIMIC IV dataset.
This benchmark offers a comprehensive and realistic assessment of LLMs' capabilities in clinical diagnosis.
We conduct a zero-shot evaluation of leading LLMs to assess their proficiency in clinical decision-making.
arXiv Detail & Related papers (2024-06-14T11:10:17Z) - Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions.
VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information.
We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
arXiv Detail & Related papers (2024-05-29T23:19:28Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z)
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.