MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models
This paper proposes MedChat, a multi-agent framework that combines specialized vision models and role-specific large language model (LLM) agents to improve automated medical diagnosis and reporting.
Why it matters
This multi-agent framework represents a novel approach to leveraging large language models and specialized AI models for improved medical diagnosis and reporting, with potential to enhance clinical accuracy and efficiency.
Key Points
- 1Integrates deep learning-based glaucoma detection with LLMs to address ophthalmologist shortages and improve clinical reporting efficiency
- 2Addresses limitations of using generalist LLMs for medical imaging, such as hallucinations, limited interpretability, and insufficient domain knowledge
- 3Employs a multi-agent design with specialized vision models and role-specific LLM agents, coordinated by a director agent
- 4Enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting tailored for clinical review and education
Details
The paper presents MedChat, a novel multi-agent framework that aims to improve automated medical diagnosis and reporting by combining specialized vision models and role-specific large language model (LLM) agents. Applying general LLMs to medical imaging tasks has been challenging due to issues like hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can reduce clinical accuracy. To address these limitations, the authors propose a multi-agent architecture where specialized vision models and role-specific LLM agents (e.g., radiologist, ophthalmologist) work together, coordinated by a director agent. This design enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting tailored for clinical review and educational use. The framework is demonstrated in the context of automated glaucoma detection, integrating deep learning-based imaging analysis with LLM-powered reasoning to mitigate ophthalmologist shortages and improve reporting efficiency.
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