MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents

MedCalc-Pro：利用大模型智能体解决复杂的医学计算问题

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.

摘要： 目前用于评估大语言模型（LLM）医学计算能力的基准测试大多基于简化设置，即每个病例对应单一计算器，且查询中明确指定了所需工具。然而，真实的临床场景往往需要多个计算器进行联合评估、嵌套量表计算，以及无法直接指定目标计算器的模糊查询。

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.

为此，我们提出了一个新的医学计算基准测试 MedCalc-Pro，它涵盖了三种难度递增的任务设置：单计算器、多计算器和嵌套计算器计算设置。MedCalc-Pro 包含 2,268 个真实临床病例，覆盖了 14 个临床科室的 77 种医学计算器。

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.

同时，为了解决现有框架和方法在复杂临床场景中表现有限的问题，我们进一步提出了一种更具通用性的智能体框架。该框架支持多工具选择和嵌套工具调用，并通过结构化验证和证据审查来抑制参数误差的传播。

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.

我们对开源、闭源以及医学专用大模型进行了系统性对比，结果表明我们的框架在所有三种任务设置中均取得了最佳性能。这项工作为评估和应用大模型处理具有挑战性的医学计算场景提供了新的基准和方法。