Summary of Cod, Towards An Interpretable Medical Agent Using Chain Of Diagnosis, by Junying Chen et al.
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
by Junying Chen, Chi Gui, Anningzhe Gao, Ke Ji, Xidong Wang, Xiang Wan, Benyou Wang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed Chain-of-Diagnosis (CoD) framework enhances the interpretability of large language models (LLMs) in medical diagnosis by transforming the diagnostic process into a transparent reasoning pathway. CoD outputs disease confidence distributions, enabling decision-making transparency and controllability. The study develops DiagnosisGPT, capable of diagnosing 9604 diseases, outperforming other LLMs on diagnostic benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical diagnosis has become more accurate with large language models (LLMs), but understanding why these models make decisions is crucial for trustworthiness. Researchers have developed a new way to explain how these models work called Chain-of-Diagnosis (CoD). This helps doctors and machines see the thought process behind each diagnosis, making it easier to identify important symptoms. The study also created DiagnosisGPT, which can diagnose many diseases better than other LLMs. |
