Summary of Argmed-agents: Explainable Clinical Decision Reasoning with Llm Disscusion Via Argumentation Schemes, by Shengxin Hong et al.
ArgMed-Agents: Explainable Clinical Decision Reasoning with LLM Disscusion via Argumentation Schemes
by Shengxin Hong, Liang Xiao, Xin Zhang, Jianxia Chen
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Symbolic Computation (cs.SC)
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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 paper presents a multi-agent framework called ArgMed-Agents that enables large language models (LLMs) to make explainable clinical decisions. The framework addresses two main barriers: LLMs’ limited performance in complex reasoning and planning, and the lack of interpretable methods for clinical decision-making. ArgMed-Agents uses an argumentation scheme to model cognitive processes in clinical reasoning, performing self-argumentation iterations and constructing a directed graph representing conflicting relationships. A symbolic solver is then used to identify rational and coherent arguments supporting decisions. The framework enables LLMs to mimic clinical argumentative reasoning by generating explanations of reasoning in a self-directed manner. Experiment results show improved accuracy in complex clinical decision-making problems, along with increased user confidence through decision explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way for big language models to make medical decisions that people can understand. Right now, these models are not very good at making complex decisions and don’t explain why they’re making those decisions. The new system, called ArgMed-Agents, helps the models work more like doctors do when they make decisions. It uses a special way of thinking about arguments to help the model make better decisions and explain them in a way that people can understand. This makes it easier for people to trust the model’s decisions. |
