  Large language models (LLMs) have recently showcased remarkable capabilities,
spanning a wide range of tasks and applications, including those in the medical
domain. Models like GPT-4 excel in medical question answering but may face
challenges in the lack of interpretability when handling complex tasks in real
clinical settings. We thus introduce the diagnostic reasoning dataset for
clinical notes (DiReCT), aiming at evaluating the reasoning ability and
interpretability of LLMs compared to human doctors. It contains 511 clinical
notes, each meticulously annotated by physicians, detailing the diagnostic
reasoning process from observations in a clinical note to the final diagnosis.
Additionally, a diagnostic knowledge graph is provided to offer essential
knowledge for reasoning, which may not be covered in the training data of
existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant
gap between their reasoning ability and that of human doctors, highlighting the
critical need for models that can reason effectively in real-world clinical
scenarios.
