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Summary of Direct: Diagnostic Reasoning For Clinical Notes Via Large Language Models, by Bowen Wang et al.


DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models

by Bowen Wang, Jiuyang Chang, Yiming Qian, Guoxin Chen, Junhao Chen, Zhouqiang Jiang, Jiahao Zhang, Yuta Nakashima, Hajime Nagahara

First submitted to arxiv on: 4 Aug 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

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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
Large language models (LLMs) have demonstrated impressive capabilities across various tasks and applications, including medical domains. Models like GPT-4 excel in medical question answering but struggle with lack of interpretability when handling complex tasks in real clinical settings. To address this challenge, we introduce the diagnostic reasoning dataset for clinical notes (DiReCT), a novel benchmark evaluating LLMs’ reasoning ability and interpretability compared to human doctors. DiReCT contains 511 clinical notes annotated by physicians, detailing the diagnostic reasoning process from observations to final diagnosis. Additionally, a diagnostic knowledge graph is provided, offering essential knowledge for reasoning that may not be covered in existing LLM training data. Evaluations of leading LLMs on DiReCT reveal a significant gap between their reasoning ability and that of human doctors, emphasizing the critical need for models that can reason effectively in real-world clinical scenarios.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about using special computer models called large language models (LLMs) to help doctors make better diagnoses. These LLMs are really good at answering medical questions, but they don’t always explain why they’re giving a certain answer. The problem is that in real hospitals, doctors need to be able to explain their thinking and reasoning when making diagnoses. To test these computer models, we created a special dataset called DiReCT with 511 clinical notes from real patient files. Each note has been carefully labeled by doctors, showing how they came up with the final diagnosis. We also gave them access to extra information that’s important for reasoning. When we tested the top LLMs on this dataset, we found that there’s a big gap between what they can do and what human doctors can do. This means we need to create computer models that can truly reason like doctors in real-world medical situations.

Keywords

* Artificial intelligence  * Gpt  * Knowledge graph  * Question answering