Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
Large language models perform well on static medical examinations, yet clinical diagnosis often requires iterative evidence gathering under uncertainty. Building on prior interactive evaluation efforts, we introduce an OSCE-inspired standardized patient simulator and a controlled, reproducible benchmark for active diagnostic inquiry. Across 468 cases and 15 models in our protocol, we observe that multi-turn evidence seeking reduces diagnostic accuracy by 12.75% and lowers supporting-evidence quality by 24.36% relative to full-context evaluation; error analyses associate these drops with premature diagnostic closure and inefficient questioning. Together, these results suggest that static full-context benchmarks may overestimate performance in interactive evidence-seeking settings, motivating complementary interactive assessment for safer clinical decision support.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 18 of its 46 references that are in the map. First posted on arXiv 21 May 2026.
Builds on
18 of 46 references.
Closest ancestors · 2
Same direction, newest first
- 2025MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports16 May
- 2025Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning11 Feb
Other references · 16
- 2024The Llama 3 Herd of Modelscited by 14,715
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- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
- 2024GPT-4o System Cardcited by 4,588
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
- 2024Qwen2.5-Coder Technical Reportcited by 1,203
- 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
- 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 872
- 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
- 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
- 2020MedDialog: Two Large-scale Medical Dialogue Datasetscited by 161
4 more
- 2025Baichuan-M1: Pushing the Medical Capability of Large Language Modelscited by 43
- 2024Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulatorcited by 39
- 2024MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotationscited by 22
- 2024MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skillscited by 17
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Citing papers per year: 2026: 1.
1 paper.
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Close in wording, no citation either way
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- 2026EviDx: Evidence-Aware Active Diagnosis with Scaffolded LLM Agents
- 2026CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation
- 2025Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models
- 2026A rubric landscape for evaluating clinical reasoning in large language models: what exists, what is missing, and what needs to be combined
- 2025ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning
- 2026Medical Reasoning with Large Language Models: A Survey and MR-Bench