MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents

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arXiv cs.AI · Siran Zhao, Ruihui Hou, Ziyue Huai, Chennuo Zhang, Tong Ruan · 2026-07-07 AI

[Submitted on 3 Jul 2026]

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Abstract:Current benchmarks for evaluating large language models (LLMs) in medical calculation are largely based on simplified settings, where each patient case corresponds to a single calculator and the required tool is explicitly specified in the query. However, real clinical scenarios often require multiple calculators for joint evaluation, nested-scale calculation, and fuzzy queries that do not directly specify the target calculator. To this end, we propose a new medical calculation benchmark, MedCalc-Pro, which covers three progressively challenging task settings: single-calculator, multi-calculator, and nested-calculator calculation settings. MedCalc-Pro contains 2,268 real-world clinical cases, covering 77 medical calculators across 14 clinical departments. Meanwhile, to address the limited performance of existing frameworks and methods in complex clinical scenarios, we further propose a more generalizable agent framework that supports multi-tool selection and nested-tool calling, while suppressing parameter error propagation through structured validation and evidence review. We conduct systematic comparisons across open-source, closed-source, and medical-specialized LLMs, and the results show that our framework achieves the best performance across all three task settings. This work provides a new benchmark and method for evaluating and applying LLMs in challenging medical calculation scenarios.

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From: Ruihui Hou [view email]
[v1] Fri, 3 Jul 2026 02:24:26 UTC (443 KB)

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추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2607.02879

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