arXiv:2607.02879v1 Announce Type: new 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
The rapid advancement of large language models (LLMs) is pushing their application into increasingly complex and sensitive domains like medical calculations, necessitating more robust evaluation benchmarks.
Improving the capability of LLMs to handle complex, real-world medical calculations is critical for their eventual adoption in clinical settings and for enhancing diagnostic and treatment support systems.
The introduction of MedCalc-Pro provides a more sophisticated benchmark, challenging LLMs beyond simplistic scenarios and accelerating the development of more practical AI agents for medical use.
- · AI developers
- · Healthcare providers
- · Medical AI companies
- · Patients
- · LLMs with limited reasoning
- · Current simplistic medical AI benchmarks
- · Manual calculation processes
MedCalc-Pro will drive the development of more capable and reliable LLM agents for complex medical tasks.
Enhanced medical LLMs could lead to improved diagnostic accuracy and personalized treatment plans, reducing medical errors.
The successful integration of AI agents into medical decision-making may transform healthcare workflows and increase accessibility to specialized medical knowledge.
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Read at arXiv cs.AI