DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
This work addresses the need for interpretable AI in real-world clinical scenarios, though it is incremental as it focuses on dataset creation and benchmarking rather than novel model development.
The authors tackled the problem of evaluating large language models' diagnostic reasoning and interpretability in clinical settings by introducing the DiReCT dataset, which contains 511 annotated clinical notes and a diagnostic knowledge graph, revealing a significant gap between model and human doctor performance.
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.