Summary of Clinicallab: Aligning Agents For Multi-departmental Clinical Diagnostics in the Real World, by Weixiang Yan et al.
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
by Weixiang Yan, Haitian Liu, Tengxiao Wu, Qian Chen, Wen Wang, Haoyuan Chai, Jiayi Wang, Weishan Zhao, Yixin Zhang, Renjun Zhang, Li Zhu, Xuandong Zhao
First submitted to arxiv on: 19 Jun 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 addresses the limitations of existing benchmarks for evaluating language models (LLMs) in clinical applications, which struggle to meet the accuracy and reliability requirements of the medical field. Current evaluation methods face data leakage or contamination risks, neglect multi-departmental specializations, rely on multiple-choice questions, and lack comprehensive evaluations of real-world scenarios. To address these limitations, the authors introduce ClinicalLab, a suite that includes ClinicalBench, an end-to-end benchmark for evaluating medical agents and LLMs, covering 24 departments and 150 diseases. The paper also proposes four novel metrics (ClinicalMetrics) for evaluating LLM performance in clinical diagnostic tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps to make language models better at doing medical work. Right now, these models are not good enough because they don’t understand how real doctors work. The authors want to fix this by creating a special test that looks more like what doctors do every day. They also want to find new ways to measure how well the models are doing. This is important because it will help make medical treatments better and safer. |
