Summary of Interpretable Differential Diagnosis with Dual-inference Large Language Models, by Shuang Zhou et al.
Interpretable Differential Diagnosis with Dual-Inference Large Language Models
by Shuang Zhou, Mingquan Lin, Sirui Ding, Jiashuo Wang, Genevieve B. Melton, James Zou, Rui Zhang
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores using large language models (LLMs) for automatic differential diagnosis (DDx), which involves generating potential diseases based on patient symptoms. The researchers curated a dataset of 570 clinical notes with expert-derived interpretations and proposed a novel framework called Dual-Inf that enables LLMs to conduct bidirectional inference for DDx interpretation. Evaluation results showed the effectiveness of Dual-Inf in predicting and elucidating differentials across four base LLMs, reducing interpretation errors and holding promise for rare disease explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses big language models to help doctors figure out what might be wrong with a patient based on their symptoms. They created a special dataset with lots of doctor notes and came up with a new way to use the models that makes it easier for doctors to understand what the models are saying. This could make it easier for doctors to decide what treatment is best for a patient. |
Keywords
» Artificial intelligence » Inference
