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A new benchmark called MedCalc-Pro addresses gaps in how large language models are evaluated on medical calculations by introducing realistic clinical scenarios that require multiple calculators, nested calculations, and ambiguous queries—moving beyond simplified single-tool evaluations. The benchmark contains 2,268 real-world clinical cases across 77 medical calculators and 14 departments, paired with a new agent framework that reduces error propagation through validation and evidence review. Testing across open-source, closed-source, and medical-specialized LLMs shows the proposed framework outperforms existing approaches on all complexity levels.
Why it matters: As healthcare systems increasingly adopt AI assistants for clinical decision support, ensuring LLMs can reliably handle complex, multi-step medical calculations with multiple interdependent tools is critical for patient safety and diagnostic accuracy.
