arXiv — NLP / Computation & Language · · 4 min read

RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Care

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Computer Science > Computation and Language

arXiv:2608.04514 (cs)
[Submitted on 5 Aug 2026]

Title:RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Care

View a PDF of the paper titled RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Care, by Mouxiao Bian and Zhi Chen and Ruiyao Chen and Lu Lu and Hengrui Liang and Chaoyi Huang and Yiluo Lin and Jingru Ding and Yun Zhong and Yuming Su and Jie Xu
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Abstract:Background: Respiratory specialty care requires multimodal interpretation, longitudinal risk assessment, guideline-concordant intervention, and whole-course management, which are poorly represented by examination-oriented medical benchmarks. Objective: To develop RESPClinBench, a real-world scenario-based benchmark for respiratory clinical decision-making, and evaluate seven contemporary large language models across AECOPD-PIM and PNBIM. Methods: RESPClinBench cases were adapted from de-identified respiratory clinical data. Three attending-level respiratory physicians revised cases, reference answers, and atomic clinical-action points, while one senior respiratory specialist performed cross-review and final adjudication. AECOPD-PIM comprised 427 open-ended COPD cases, and PNBIM comprised 196 multimodal pulmonary nodule cases combining chest CT with structured clinical information. Seven models generated 4,361 responses through standardized API inference with temperature 0 and a maximum output length of 8192 tokens. An automated framework calculated the final score as the arithmetic mean of atomic-action recall and rubric-based LLM-as-a-Judge assessment. Results: Across 623 cases, the mean final score was 68.58. Qwen3.6-27B ranked first overall at 71.22, Qwen3.5-397B-A17B led PNBIM at 72.48, and Qwen3.6-27B led AECOPD-PIM at 71.11. Imaging hallucination and serious medical risk occurred in 31.85% and 8.16% of PNBIM responses; medication-safety risk and serious medical risk occurred in 26.93% and 1.44% of AECOPD-PIM responses. Conclusions: RESPClinBench identifies task-specific limitations in multimodal pulmonary nodule assessment and longitudinal COPD management. Combining explicit clinical-action coverage, holistic evaluation, and independent safety flags provides a clinically grounded basis for model selection and prospective validation.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.04514 [cs.CL]
  (or arXiv:2608.04514v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.04514
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jie Xu [view email]
[v1] Wed, 5 Aug 2026 06:49:30 UTC (1,120 KB)
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