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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language

Yuhe Ke1,2, Rui Yang1, Sui An Lie2

  • 1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

Journal of Medical Internet Research
|November 19, 2024
PubMed
Summary
This summary is machine-generated.

Large language models in a multi-agent framework improved diagnostic accuracy by correcting cognitive biases. This AI approach outperformed human diagnostic capabilities in challenging medical cases.

Keywords:
clinical decision-makingcognitive biasgenerative artificial intelligencelarge language modelmulti-agent

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Cognitive Bias Research

Background:

  • Cognitive biases frequently lead to diagnostic errors and negatively impact patient outcomes.
  • Mitigating these biases in clinical decision-making is a significant challenge for healthcare professionals.

Purpose of the Study:

  • To investigate the potential of large language models (LLMs) within a multi-agent framework to reduce cognitive biases.
  • To simulate clinical decision-making using multi-agent conversations and assess improvements in diagnostic accuracy compared to human performance.

Main Methods:

  • Utilized GPT-4 to create a multi-agent framework simulating clinical team dynamics.
  • Assigned roles to agents including diagnosis, devil's advocate, field expert, discussion facilitator, and summarizer.
  • Evaluated diagnostic accuracy of various agent configurations on 16 case reports involving misdiagnoses due to cognitive biases.

Main Results:

  • The initial diagnosis accuracy was 0%.
  • The best multi-agent framework achieved 76% accuracy for the top 2 differential diagnoses after discussions.
  • This AI-driven accuracy was significantly higher than that of human evaluators (OR 3.49, P=.002).

Conclusions:

  • The multi-agent framework effectively re-evaluated and corrected diagnostic misconceptions.
  • LLM-driven multi-agent conversations show promise for improving diagnostic accuracy in complex medical scenarios.