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MedCalc-Eval and MedCalc-Env: Advancing Medical Calculation Capabilities of Large Language Models

arXiv 2510.27267 ↗First posted 31 Oct 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

As large language models (LLMs) enter the medical domain, most benchmarks evaluate them on question answering or descriptive reasoning, overlooking quantitative reasoning critical to clinical decision-making. Existing datasets like MedCalc-Bench cover few calculation tasks and fail to reflect real-world computational scenarios. We introduce MedCalc-Eval, the largest benchmark for assessing LLMs' medical calculation abilities, comprising 700+ tasks across two types: equation-based (e.g., Cockcroft-Gault, BMI, BSA) and rule-based scoring systems (e.g., Apgar, Glasgow Coma Scale). These tasks span diverse specialties including internal medicine, surgery, pediatrics, and cardiology, offering a broader and more challenging evaluation setting. To improve performance, we further develop MedCalc-Env, a reinforcement learning environment built on the InternBootcamp framework, enabling multi-step clinical reasoning and planning. Fine-tuning a Qwen2.5-32B model within this environment achieves state-of-the-art results on MedCalc-Eval, with notable gains in numerical sensitivity, formula selection, and reasoning robustness. Remaining challenges include unit conversion, multi-condition logic, and contextual understanding. Code and datasets are available at https://github.com/maokangkun/MedCalc-Eval.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 31 Oct 2025.

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References · 13

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  3. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  4. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMscited by 289
  5. 2025Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Modelscited by 144
  6. 2025MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphscited by 110
  7. 2024MedCalc-Bench: Evaluating Large Language Models for Medical Calculationscited by 70
  8. 2025Baichuan-M2: Scaling Medical Capability with Large Verifier Systemcited by 59
  9. 2024MedCoT: Medical Chain of Thought via Hierarchical Expertcited by 46
  10. 2025Baichuan-M1: Pushing the Medical Capability of Large Language Modelscited by 43
  11. 2025InternBootcamp: Boosting LLM Reasoning with Verifiable Task Scalingcited by 20
  12. 2025Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learningcited by 14
1 more
  1. 2025From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculationscited by 11

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  1. 2026Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
  2. 2026MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents
  3. 2025Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
  4. 2026Improving Medical Calculation of LLMs with Embedded Coding
  5. 2025Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
  6. 2025Gazal-R1: Achieving State-of-the-Art Medical Reasoning with Parameter-Efficient Two-Stage Training
  7. 2025MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
  8. 2025Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning