Conversational Health Agents: A Personalized LLM-Powered Agent Framework
This work addresses the problem of limited agent capabilities in healthcare conversational systems for developers and users, though it appears incremental as it builds on existing LLM-based approaches.
The authors tackled the limitations of current Conversational Health Agents (CHAs) by proposing openCHA, an open-source LLM-powered framework that enhances agent capabilities for personalized healthcare responses, demonstrated through two demonstrations and four use cases.
Conversational Health Agents (CHAs) are interactive systems that provide healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically lacking multi-step problem-solving, personalized conversations, and multimodal data analysis. Our aim is to overcome these limitations. We propose openCHA, an open-source LLM-powered framework, to empower conversational agents to generate a personalized response for users' healthcare queries. This framework enables developers to integrate external sources including data sources, knowledge bases, and analysis models, into their LLM-based solutions. openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries. It facilitates knowledge acquisition, problem-solving capabilities, multilingual and multimodal conversations, and fosters interaction with various AI platforms. We illustrate the framework's proficiency in handling complex healthcare tasks via two demonstrations and four use cases. Moreover, we release openCHA as open source available to the community via GitHub.