KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning

• URL: http://arxiv.org/abs/2509.24816v1
• Date: Mon, 29 Sep 2025 14:03:01 GMT
• Title: KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning
• Authors: Xilin Dang, Kexin Chen, Xiaorui Su, Ayush Noori, Iñaki Arango, Lucas Vittor, Xinyi Long, Yuyang Du, Marinka Zitnik, Pheng Ann Heng,
• Abstract summary: In clinical practice, physicians refrain from making decisions when patient information is insufficient.<n>This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses.<n>We propose KnowGuard, a novel paradigm that integrates systematic knowledge graph exploration for clinical decision-making.
• Score: 44.49237466254508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical scenarios. However, existing LLMs struggle with the abstentions, frequently providing overconfident responses despite incomplete information. This limitation stems from conventional abstention methods relying solely on model self-assessments, which lack systematic strategies to identify knowledge boundaries with external medical evidences. To address this, we propose \textbf{KnowGuard}, a novel \textit{investigate-before-abstain} paradigm that integrates systematic knowledge graph exploration for clinical decision-making. Our approach consists of two key stages operating on a shared contextualized evidence pool: 1) an evidence discovery stage that systematically explores the medical knowledge space through graph expansion and direct retrieval, and 2) an evidence evaluation stage that ranks evidence using multiple factors to adapt exploration based on patient context and conversation history. This two-stage approach enables systematic knowledge graph exploration, allowing models to trace structured reasoning paths and recognize insufficient medical evidence. We evaluate our abstention approach using open-ended multi-round clinical benchmarks that mimic realistic diagnostic scenarios, assessing abstention quality through accuracy-efficiency trade-offs beyond existing closed-form evaluations. Experimental evidences clearly demonstrate that KnowGuard outperforms state-of-the-art abstention approaches, improving diagnostic accuracy by 3.93\% while reducing unnecessary interaction by 7.27 turns on average.

Related papers

• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models [48.95516224614331]
  We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation.<n>Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical, and implicit adherence to safety protocols.
  arXiv  Detail & Related papers  (2026-01-11T02:20:40Z)
• MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring [39.776843923694244]
  We propose MedKGI, a diagnostic framework grounded in clinical practices.<n>We show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency.
  arXiv  Detail & Related papers  (2025-12-30T12:31:53Z)
• 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)
• MedAgentAudit: Diagnosing and Quantifying Collaborative Failure Modes in Medical Multi-Agent Systems [28.028343705313805]
  Large language model (LLM)-based multi-agent systems show promise in simulating medical consultations.<n>But their evaluation is often confined to final-answer accuracy.<n>This practice treats their internal collaborative processes as opaque "black boxes"
  arXiv  Detail & Related papers  (2025-10-11T11:48:57Z)
• 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)
• Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning [8.35131510062609]
  Large language models (LLMs) show promise for diagnostic reasoning but often lack reliable, knowledge grounded inference.<n>We treat the LLM as a reward model of KG reasoning paths, where the model learns to judge whether a candidate path leads to correct diagnosis for a given patient input.<n>Our finding provides the first systematic assessment of "reward model style" reasoning over clinical KGs.
  arXiv  Detail & Related papers  (2025-09-22T18:39:09Z)
• 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)
• Uncertainty-aware abstention in medical diagnosis based on medical texts [87.88110503208016]
  This study addresses the critical issue of reliability for AI-assisted medical diagnosis.<n>We focus on the selection prediction approach that allows the diagnosis system to abstain from providing the decision if it is not confident in the diagnosis.<n>We introduce HUQ-2, a new state-of-the-art method for enhancing reliability in selective prediction tasks.
  arXiv  Detail & Related papers  (2025-02-25T10:15:21Z)
• Towards Reducing Diagnostic Errors with Interpretable Risk Prediction [18.474645862061426]
  We propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses. Our ultimate aim is to increase access to evidence and reduce diagnostic errors.
  arXiv  Detail & Related papers  (2024-02-15T17:05:48Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• 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)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

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.