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Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care

arXiv AI Archived May 12, 2026 ✓ Full text saved

arXiv:2605.08533v1 Announce Type: new Abstract: 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 MI

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    Computer Science > Artificial Intelligence [Submitted on 8 May 2026] Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care Burcu Sayin, Ngoc Vo Hong, Ipek Baris Schlicht, Jacopo Staiano, Pasquale Minervini, Sara Allievi, Nicola Susca, Nicola Osti, Alberto Maino, Vito Racanelli, Andrea Passerini 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. Comments: Paper under review Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.08533 [cs.AI]   (or arXiv:2605.08533v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.08533 Focus to learn more Submission history From: Burcu Sayin Günel [view email] [v1] Fri, 8 May 2026 22:40:10 UTC (1,666 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    May 12, 2026
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    May 12, 2026
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