  We present MedConceptsQA, a dedicated open source benchmark for medical
concepts question answering. The benchmark comprises of questions of various
medical concepts across different vocabularies: diagnoses, procedures, and
drugs. The questions are categorized into three levels of difficulty: easy,
medium, and hard. We conducted evaluations of the benchmark using various Large
Language Models. Our findings show that pre-trained clinical Large Language
Models achieved accuracy levels close to random guessing on this benchmark,
despite being pre-trained on medical data. However, GPT-4 achieves an absolute
average improvement of nearly 27%-37% (27% for zero-shot learning and 37% for
few-shot learning) when compared to clinical Large Language Models. Our
benchmark serves as a valuable resource for evaluating the understanding and
reasoning of medical concepts by Large Language Models. Our benchmark is
available at https://huggingface.co/datasets/ofir408/MedConceptsQA
