mlsanityData to 7 Oct 2026

Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care

arXiv 2605.08533 ↗First posted 8 May 2026cs.AIcited by 0

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Abstract (arXiv)

Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as interactive aids in live physician workflows remains sparse. MedSyn lets physicians iteratively query an LLM provided with the full clinical record while initially viewing only the chief complaint. Seven physicians (three seniors, four residents) completed baseline and AI-assisted sessions across 52 MIMIC-IV cases stratified by difficulty. Blinded evaluation showed residents' Hard-case correctness rose from 0.589 to 0.734; difficulty-standardised completely-correct rates confirmed a medium effect (Delta = 0.092; p = 0.071; d = 0.47). Automated metrics corroborated these gains: standardised any-match accuracy improved by 0.156 (p < 0.0001), and residents showed the largest F1 gain (Delta = 0.138; p < 0.0001). Dialogue analysis revealed expertise-dependent strategies (seniors asked targeted, hypothesis-driven questions; residents relied on broader queries) and cross-expertise concordance increased (Delta = 0.145; p < 0.0001). Interactive LLM support meaningfully enhances diagnostic reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 8 May 2026.

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9 of 23 references.

References · 9

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  2. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  3. 2024MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Makingcited by 162
  4. 2023Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaborationcited by 70
  5. 2024Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithmcited by 14
  6. 2024LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Themcited by 5
  7. 2024A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Makingcited by 5
  8. 2025Streamlining evidence based clinical recommendations with large language modelscited by 3
  9. 2025MedSyn: Enhancing Diagnostics with Human-AI Collaborationcited by 2

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  1. 2025ER-Reason: A Benchmark Dataset for LLM Clinical Reasoning in the Emergency Room
  2. 2025Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
  3. 2024Superhuman performance of a large language model on the reasoning tasks of a physician
  4. 2025Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
  5. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning
  6. 2026Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
  7. 2026Safety and accuracy follow different scaling laws in clinical large language models
  8. 2026MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models