ClinicalAgents framework enhances AI clinical diagnosis with dual-memory

Researchers have developed ClinicalAgents, a new multi-agent framework that mimics the iterative reasoning process of human clinicians for improved diagnostic accuracy. This system utilizes a dual-memory architecture, combining a dynamic working memory for patient state and a static experience memory for retrieving guidelines and past cases. Experiments show ClinicalAgents outperforms existing single-agent and multi-agent baselines in both diagnostic accuracy and explainability. AI

IMPACT This framework could lead to more accurate and explainable AI-driven clinical decision support systems.

RANK_REASON This is a research paper detailing a novel AI framework for clinical decision making. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• ClinicalAgents
• Large Language Models
• Monte Carlo Tree Search

• paper
• model release

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