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Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Conversational health agents: a personalized large language model-powered agent framework.

Mahyar Abbasian1, Iman Azimi1, Amir M Rahmani1,2

  • 1Department of Computer Science, University of California Irvine, Irvine, CA 92697-2625, United States.

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|July 7, 2025
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Summary
This summary is machine-generated.

The openCHA framework enhances conversational health agents (CHAs) with advanced problem-solving and multimodal analysis. This open-source solution significantly improves accuracy in areas like diabetic management and mental health evaluation compared to existing models.

Keywords:
artificial intelligenceconversational health agentslarge language modelsopen-source agentsopenCHA

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Natural Language Processing
  • Machine Learning for Medical Applications

Background:

  • Current Conversational Health Agents (CHAs), particularly those using Large Language Models (LLMs), are limited in multistep problem-solving, personalization, and multimodal data analysis.
  • There is a need for a more robust and customizable framework to develop advanced CHAs.

Purpose of the Study:

  • To introduce openCHA, an open-source LLM-powered framework designed to overcome the limitations of existing CHAs.
  • To enable the development of CHAs with enhanced capabilities, including knowledge acquisition, problem-solving, multilingual, and multimodal interactions.

Main Methods:

  • Developed openCHA, an open-source framework providing a foundational architecture and codebase for building customizable CHAs.
  • Integrated LLMs with external data sources to enable features like explainability, personalization, and reliability.
  • Leveraged capabilities for knowledge acquisition, problem-solving, and multilingual/multimodal conversations.

Main Results:

  • Demonstrated openCHA's effectiveness across multiple health domains with 2 demos and 5 use cases.
  • Achieved 92.1% accuracy in diabetic patient management, significantly outperforming GPT-4 (51.8%).
  • Outperformed GPT-4 in food recommendations and excelled in mental health chatbot evaluation with the lowest Mean Absolute Error (0.31).

Conclusions:

  • The openCHA framework empowers the development of a diverse range of CHAs for various healthcare tasks.
  • openCHA enhances CHAs with explainability, personalization, and reliability, addressing key limitations of current systems.
  • Future work will focus on improving planning robustness, accuracy, and handling user query ambiguity.