MedConsultBench: A Full-Cycle, Fine-Grained, Process-Aware Benchmark for Medical Consultation Agents

• URL: http://arxiv.org/abs/2601.12661v1
• Date: Mon, 19 Jan 2026 02:18:10 GMT
• Title: MedConsultBench: A Full-Cycle, Fine-Grained, Process-Aware Benchmark for Medical Consultation Agents
• Authors: Chuhan Qiao, Jianghua Huang, Daxing Zhao, Ziding Liu, Yanjun Shen, Bing Cheng, Wei Lin, Kai Wu,
• Abstract summary: We propose MedConsultBench, a comprehensive framework designed to evaluate the complete online consultation cycle.<n>Our methodology introduces Atomic Information Units (AIUs) to track clinical information acquisition at a sub-turn level.<n>By addressing the underspecification and ambiguity inherent in online consultations, the benchmark evaluates uncertainty-aware yet concise inquiry.
• Score: 10.109613967215447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current evaluations of medical consultation agents often prioritize outcome-oriented tasks, frequently overlooking the end-to-end process integrity and clinical safety essential for real-world practice. While recent interactive benchmarks have introduced dynamic scenarios, they often remain fragmented and coarse-grained, failing to capture the structured inquiry logic and diagnostic rigor required in professional consultations. To bridge this gap, we propose MedConsultBench, a comprehensive framework designed to evaluate the complete online consultation cycle by covering the entire clinical workflow from history taking and diagnosis to treatment planning and follow-up Q\&A. Our methodology introduces Atomic Information Units (AIUs) to track clinical information acquisition at a sub-turn level, enabling precise monitoring of how key facts are elicited through 22 fine-grained metrics. By addressing the underspecification and ambiguity inherent in online consultations, the benchmark evaluates uncertainty-aware yet concise inquiry while emphasizing medication regimen compatibility and the ability to handle realistic post-prescription follow-up Q\&A via constraint-respecting plan revisions. Systematic evaluation of 19 large language models reveals that high diagnostic accuracy often masks significant deficiencies in information-gathering efficiency and medication safety. These results underscore a critical gap between theoretical medical knowledge and clinical practice ability, establishing MedConsultBench as a rigorous foundation for aligning medical AI with the nuanced requirements of real-world clinical care.

Related papers

• MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation [5.601620793903095]
  We propose MIND, a unified inquiry--diagnosis reinforcement learning framework for psychiatric consultation.<n>Specifically, we build a Criteria-Grounded Psychiatric Reasoning Bank (PRB) that summarizes dialogue context into clinical retrieval states.<n>Building on this foundation, MIND enforces explicit clinical reasoning with rubric-based process rewards to provide fine-grained supervision over intermediate decision steps.
  arXiv  Detail & Related papers  (2026-03-04T03:05:38Z)
• ClinConsensus: A Consensus-Based Benchmark for Evaluating Chinese Medical LLMs across Difficulty Levels [39.33170904610862]
  Large language models (LLMs) are increasingly applied to health management, showing promise across disease prevention, clinical decision-making, and long-term care.<n>We introduce ClinConsensus, a Chinese medical benchmark curated, validated and quality-controlled by clinical experts.<n> ClinConsensus comprises 2500 open-ended cases spanning the full continuum of care--from prevention and intervention to long-term follow-up--covering 36 medical specialties, 12 common clinical task types, and progressively increasing levels of complexity.
  arXiv  Detail & Related papers  (2026-03-02T17:17:18Z)
• 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)
• 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)
• MedKGEval: A Knowledge Graph-Based Multi-Turn Evaluation Framework for Open-Ended Patient Interactions with Clinical LLMs [19.12790150016383]
  We present MedKGEval, a novel multi-turn evaluation framework for clinical large language models.<n>A knowledge graph-driven patient simulation mechanism retrieves relevant medical facts from a curated knowledge graph.<n>A turn-level evaluation framework assesses each model response for clinical appropriateness, factual correctness, and safety.
  arXiv  Detail & Related papers  (2025-10-14T07:22:26Z)
• 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)
• 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)
• Systematic Literature Review on Clinical Trial Eligibility Matching [0.24554686192257422]
  Review highlights how explainable AI and standardized ontology can bolster clinician trust and broaden adoption.<n>Further research into advanced semantic and temporal representations, expanded data integration, and rigorous prospective evaluations is necessary to fully realize the transformative potential of NLP in clinical trial recruitment.
  arXiv  Detail & Related papers  (2025-03-02T11:45:50Z)
• Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation [31.061600616994145]
  HDCEval is built on a set of fine-grained medical evaluation guidelines developed in collaboration with professional doctors.<n>The framework decomposes complex evaluation tasks into specialized subtasks, each evaluated by expert models.<n>This hierarchical approach ensures that each aspect of the evaluation is handled with expert precision, leading to a significant improvement in alignment with human evaluators.
  arXiv  Detail & Related papers  (2025-01-12T07:30:49Z)
• MedCoT: Medical Chain of Thought via Hierarchical Expert [48.91966620985221]
  This paper presents MedCoT, a novel hierarchical expert verification reasoning chain method.<n>It is designed to enhance interpretability and accuracy in biomedical imaging inquiries.<n> Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-12-18T11:14:02Z)
• Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence [68.05876437208505]
  We present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow.<n>We also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses.
  arXiv  Detail & Related papers  (2024-12-02T15:25:02Z)
• A Methodology for Bi-Directional Knowledge-Based Assessment of Compliance to Continuous Application of Clinical Guidelines [1.52292571922932]
  We introduce a new approach for automated guideline-based quality assessment of the care process. The BiKBAC method assesses the degree of compliance when applying clinical guidelines. The DiscovErr system was evaluated in a separate study in the type 2 diabetes management domain.
  arXiv  Detail & Related papers  (2021-03-13T20:43:45Z)

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.