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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Hierarchical agent reflection for aligning LLM reasoning with clinical diagnostic processes.

Xinda Wang1, Xiaotong Li2, Deng Zhao3

  • 1School of Software Technology, Zhejiang University, Hangzhou, China.

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|January 7, 2026
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Summary
This summary is machine-generated.

This study trains large language models on clinician reasoning notes to improve AI diagnostic accuracy. The new method enhances transparency and interpretability in AI-assisted medical diagnosis.

Keywords:
Agent systemClinical diagnosisLarge language modelsReasoning and reflection

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Natural Language Processing

Background:

  • Traditional diagnostic models lack transparency and fail to replicate clinician reasoning.
  • Interpreting and justifying AI diagnostic outputs remains a significant challenge.

Purpose of the Study:

  • To develop a method for generating clinical notes that capture diagnostic reasoning.
  • To train large language models (LLMs) to mimic human step-by-step diagnostic processes.
  • To enhance the transparency and interpretability of AI-assisted medical diagnosis.

Main Methods:

  • A hierarchical agent reflection mechanism was introduced to generate detailed clinical notes.
  • These notes deconstruct the diagnostic process into stages handled by specialized agents.
  • LLMs were trained on these clinician-mimicking notes.

Main Results:

  • Models trained with the proposed method outperformed general and domain-specific LLMs in diagnostic tasks.
  • The approach successfully aligned AI reasoning with human clinical practice.
  • Generated clinical notes improved accuracy and reliability.

Conclusions:

  • The novel method enhances diagnostic transparency and interpretability in AI.
  • This approach offers a valuable tool for AI-assisted clinical decision-making.
  • Mimicking clinician reasoning improves AI diagnostic performance and trustworthiness.