Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning

• URL: http://arxiv.org/abs/2506.13474v1
• Date: Mon, 16 Jun 2025 13:32:01 GMT
• Title: Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning
• Authors: David Bani-Harouni, Chantal Pellegrini, Ege Özsoy, Matthias Keicher, Nassir Navab,
• Abstract summary: 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.
• Score: 38.49879425944787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited "out-of-the-box" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases containing various clinical tests and show the benefit of explicitly training clinical decision-making for increasing diagnostic performance and efficiency.

Related papers

• 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)
• Integrating clinical reasoning into large language model-based diagnosis through etiology-aware attention steering [7.092919468004549]
  Large Language Models (LLMs) demonstrate significant capabilities in medical text understanding and generation.<n>This study aims to enhance LLMs' diagnostic accuracy and clinical reasoning ability.
  arXiv  Detail & Related papers  (2025-08-01T03:05:43Z)
• DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction [6.253071540087993]
  We propose DiaLLM, the first medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues.<n>To construct clinically grounded dialogues from EHR, we design a Clinical Test Reference (CTR) strategy that maps each clinical code to its corresponding description and classifies test results as "normal" or "abnormal"<n>Extensive experimental results demonstrate that DiaLLM outperforms baselines in clinical test recommendation and diagnosis prediction.
  arXiv  Detail & Related papers  (2025-06-24T23:47:21Z)
• Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning [37.37330596550283]
  We introduce a framework for reliable medical image diagnosis using vision-language models.<n>A test-time scaling strategy consolidates multiple candidate outputs into a reliable final diagnosis.<n>We evaluate our approach across various medical imaging modalities.
  arXiv  Detail & Related papers  (2025-06-11T22:23:38Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking [58.25862290294702]
  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)
• 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)
• 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)
• Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales [15.362903610463285]
  We present a "reasoning-aware" diagnosis framework that rationalizes the diagnostic process via prompt-based learning. We propose a novel set of criteria for evaluating machine-generated rationales' potential for real-world clinical settings.
  arXiv  Detail & Related papers  (2023-12-12T16:14:45Z)
• A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
  We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
  arXiv  Detail & Related papers  (2023-06-01T16:23:47Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.