Summary of Medcalc-bench: Evaluating Large Language Models For Medical Calculations, by Nikhil Khandekar et al.
MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
by Nikhil Khandekar, Qiao Jin, Guangzhi Xiong, Soren Dunn, Serina S Applebaum, Zain Anwar, Maame Sarfo-Gyamfi, Conrad W Safranek, Abid A Anwar, Andrew Zhang, Aidan Gilson, Maxwell B Singer, Amisha Dave, Andrew Taylor, Aidong Zhang, Qingyu Chen, Zhiyong Lu
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes MedCalc-Bench, a novel dataset designed to evaluate the medical calculation capabilities of large language models (LLMs). Currently, benchmarks primarily focus on question-answering and descriptive reasoning in medicine. However, in real-world scenarios, doctors often rely on clinical calculators that utilize quantitative equations and rule-based reasoning for evidence-based decision support. MedCalc-Bench contains over 1000 manually reviewed instances from 55 different medical calculation tasks, each consisting of a patient note, question, ground truth answer, and step-by-step explanation. The evaluation results show promising potential but highlight the need for improvement in extracting correct entities, using the right equations, and performing arithmetic calculations accurately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new dataset called MedCalc-Bench to help large language models understand medical math problems. Right now, most tests ask models to answer questions about medicine, which is important. But doctors actually use special calculators that follow formulas and rules to make decisions. The new dataset has over 1,000 examples of different medical calculations, like calculating a patient’s heart rate or blood pressure. The results show that these language models are getting better but still need help with understanding what numbers mean, using the right math formulas, and doing simple math correctly. |
Keywords
» Artificial intelligence » Question answering
