Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias

Understand

Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes.

• Addressing these biases presents a formidable challenge in the medical field.
• Objective: This study explores the role of large language models (LLMs) in mitigating these biases through the utilization of a multi-agent framework.
• We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy.