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The paper introduces openCHA, an open-source LLM-powered framework designed to facilitate the development of personalized conversational health agents (CHAs) with enhanced capabilities beyond standard LLM-based systems. openCHA integrates knowledge acquisition, problem-solving, multilingual/multimodal conversation, and AI platform interaction to address limitations in multistep problem-solving, personalization, and multimodal data analysis in existing CHAs. Experiments across multiple health domains, including diabetic patient management, food recommendations, mental health chatbot evaluation, and physiological data analysis, demonstrate openCHA's superior performance compared to GPT-4 and other models.
An open-source framework lets you build conversational health agents that crush GPT-4 on tasks like diabetes management and mental health evaluation.
Abstract Objective Conversational Health Agents (CHAs) are interactive systems providing 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 needing more multistep problem-solving, personalized conversations, and multimodal data analysis. We aim to overcome these limitations. Materials and methods We propose openCHA, an open-source LLM-powered framework, designed to enable the development of conversational agents. OpenCHA offers a foundational and structured architecture and codebase, enabling researchers and developers to build and customize their CHA based on the specifics of their intended application. The framework leverages knowledge acquisition, problem-solving capabilities, multilingual, and multimodal conversations, and allows interaction with various AI platforms. We have released the framework as open source for the community on GitHub (https://github.com/Institute4FutureHealth/CHA and https://opencha.com). Results We demonstrated the openCHA’s capability to develop CHAs across multiple health domains using 2 demos and 5 use cases. In diabetic patient management, developed CHA achieved a 92.1% accuracy rate, surpassing GPT4’s 51.8%. In food recommendations, developed CHA outperformed GPT4. The developed CHA excelled as an evaluator for mental health chatbots, recording the lowest Mean Absolute Error at 0.31, compared to competitors like GPT, Misteral, Gemini, and Claude. Additionally, the empathy enabled CHA identified emotional states with 89% accuracy, and in physiological data analysis of heart rate from Photoplethysmography (PPG) signals, the developed CHA achieved an mean absolute error of 2.83, far lower than GPT-4o’s 8.93. Discussion The openCHA framework enhances CHAs by enabling features such as explainability, personalization, and reliability through its integration with LLMs and external data sources. The developed CHAs face challenges like latency, token limits, and scalability. Future efforts will focus on improving planning robustness, enhancing accuracy and evaluation methods, and resolving user query ambiguity to further refine the framework’s effectiveness. Conclusion The diverse demos and use cases of openCHA demonstrate the framework’s capacity to empower the development of a wide range of CHAs for various healthcare tasks.
