MyoCardBench: A Real-World Data Benchmark for Evaluating Large Language Models in Clinically Authentic Cardiovascular Care Scenarios
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Computer Science > Computation and Language
arXiv:2607.25186 (cs)
[Submitted on 28 Jul 2026]
Title:MyoCardBench: A Real-World Data Benchmark for Evaluating Large Language Models in Clinically Authentic Cardiovascular Care Scenarios
Authors:Xiao Li, Mouxiao Bian, Zhaodi Wu, Sijie Ren, Juechen Chen, Lu Lu, Jingru Ding, Yun Zhong, Jie Xu, Yixiu Liang, Junbo Ge
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Abstract:Background: Most medical large language model (LLM) benchmarks focus on examination knowledge or isolated tasks and may not reflect the longitudinal, multimodal, and safety-critical workflow of cardiovascular care. Objective: To develop MyoCardBench, a real-world benchmark spanning the cardiovascular care continuum, and assess LLM performance across clinical dimensions and specialist tasks. Methods: MyoCardBench includes 2,263 items from 13 task-specific datasets derived from de-identified cardiovascular records and examination data. Sixteen cardiology physicians conducted annotation and reference construction, followed by cross-review from two senior cardiologists. Seven LLMs generated 15,841 outputs under standardized zero-shot settings. Open-ended tasks were evaluated using key-point coverage and holistic clinical quality, while CardioEthics was scored by accuracy. Results: GPT-5.4 achieved the highest macro-average (62.55) and item-weighted mean (62.19), followed by Gemini 3.1 Pro (59.95) and Qwen 3.6 27B (59.72). GPT-5.4 ranked first in all three dimensions. CardioAuxReport performed best (86.38), whereas CardioECGRead (17.25) and CardioEthics (17.34) were lowest. The largest gaps between holistic clinical quality and key-point coverage occurred in CardioComm (52.71), CardioEmergRescue (52.05), and CardioTreatPlan (48.80). Conclusions: To our knowledge, MyoCardBench is the largest real-world, multi-task benchmark for LLM evaluation across the cardiovascular care continuum and offers the broadest coverage of clinically authentic cardiology scenarios reported to date. It provides a rigorous framework for identifying model strengths, clinically important omissions, and priorities for future development.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.25186 [cs.CL] |
| (or arXiv:2607.25186v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.25186
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled MyoCardBench: A Real-World Data Benchmark for Evaluating Large Language Models in Clinically Authentic Cardiovascular Care Scenarios, by Xiao Li and Mouxiao Bian and Zhaodi Wu and Sijie Ren and Juechen Chen and Lu Lu and Jingru Ding and Yun Zhong and Jie Xu and Yixiu Liang and Junbo Ge
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