# Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care

arXiv: https://arxiv.org/abs/2605.08533 · HTML: https://mlsanity.com/paper/2605.08533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 9 of 9)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://mlsanity.com/paper/2310.00280/) (2023)
- [Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm](https://mlsanity.com/paper/2403.16446/) (2024)
- [LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them](https://mlsanity.com/paper/2406.18034/) (2024)
- [A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making](https://mlsanity.com/paper/2411.00248/) (2024)
- [Streamlining evidence based clinical recommendations with large language models](https://mlsanity.com/paper/2505.10282/) (2025)
- [MedSyn: Enhancing Diagnostics with Human-AI Collaboration](https://mlsanity.com/paper/2506.14774/) (2025)

## 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.