Summary of Beyond Direct Diagnosis: Llm-based Multi-specialist Agent Consultation For Automatic Diagnosis, by Haochun Wang et al.
Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
by Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, Ting Liu
First submitted to arxiv on: 29 Jan 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 The proposed Agent-derived Multi-Specialist Consultation (AMSC) framework leverages large language models as medical practitioners to model the real-world diagnosis process. By adaptively fusing probability distributions from multiple agents, the approach demonstrates superiority over baselines while requiring less parameter updates and training time. The study also explores the role of implicit symptoms in automatic diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs called “large language models” to help doctors diagnose patients’ illnesses. Normally, patients see a general doctor first, then get sent to specialists for more help if needed. The AI models work like doctors, talking to each other and sharing ideas to figure out what’s wrong with the patient. This new approach is faster and better than old methods, and it also looks at hidden clues that might be important in making a diagnosis. |
Keywords
» Artificial intelligence » Probability
