  LLMs have demonstrated impressive performance in answering medical questions,
such as achieving passing scores on medical licensing examinations. However,
medical board exam or general clinical questions do not capture the complexity
of realistic clinical cases. Moreover, the lack of reference explanations means
we cannot easily evaluate the reasoning of model decisions, a crucial component
of supporting doctors in making complex medical decisions. To address these
challenges, we construct two new datasets: JAMA Clinical Challenge and
Medbullets. JAMA Clinical Challenge consists of questions based on challenging
clinical cases, while Medbullets comprises simulated clinical questions. Both
datasets are structured as multiple-choice question-answering tasks,
accompanied by expert-written explanations. We evaluate seven LLMs on the two
datasets using various prompts. Experiments demonstrate that our datasets are
harder than previous benchmarks. Human and automatic evaluations of
model-generated explanations provide insights into the promise and deficiency
of LLMs for explainable medical QA.
