MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents
A new benchmark MedCalc-Pro evaluates LLMs on complex medical calculations involving multiple calculators, nested scales, and fuzzy queries. The authors also propose a generalizable agent framework that outperforms existing models on all three task settings.
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[Submitted on 3 Jul 2026]
Title:MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.02879 [cs.AI]
(or arXiv:2607.02879v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02879
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ruihui Hou [view email] [v1] Fri, 3 Jul 2026 02:24:26 UTC (443 KB)
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