医疗保健中的通用人工智能虚拟形象
A General-purpose AI Avatar in Healthcare
AI总结：
本文提出一种医疗通用AI虚拟形象框架，通过三类别提示词典、提示改进机制和两阶段微调方法构建具有个性的聊天机器人，以提升患者参与度，并展望了上下文理解与医学数据微调等未来方向。
摘要：
机器学习和自然语言处理的最新进展推动了人工智能（AI）作为医疗保健行业重要工具的快速发展。使用大语言模型（LLMs）作为对话代理或聊天机器人，有潜力协助医生诊断患者、检测疾病的早期症状，并向患者提供健康建议。本文聚焦于聊天机器人在医疗保健中的作用，并探索使用虚拟形象使AI交互对患者更具吸引力。通过使用一个三类别提示词典和提示改进机制，展示了一个通用AI虚拟形象应用的框架。提出了一种两阶段方法来微调通用AI语言模型，并创建不同的AI虚拟形象与用户讨论医疗问题。提示工程增强了聊天机器人的对话能力和个性特征，促进了与患者之间更人性化的互动。最终，向聊天机器人注入个性可能会提高患者的参与度。未来的研究方向包括探索改进聊天机器人对上下文理解的方法，以及通过使用专门的医学数据集进行微调来确保其输出的准确性。
英文摘要：
Recent advancements in machine learning and natural language processing have led to the rapid development of artificial intelligence (AI) as a valuable tool in the healthcare industry. Using large language models (LLMs) as conversational agents or chatbots has the potential to assist doctors in diagnosing patients, detecting early symptoms of diseases, and providing health advice to patients. This paper focuses on the role of chatbots in healthcare and explores the use of avatars to make AI interactions more appealing to patients. A framework of a general-purpose AI avatar application is demonstrated by using a three-category prompt dictionary and prompt improvement mechanism. A two-phase approach is suggested to fine-tune a general-purpose AI language model and create different AI avatars to discuss medical issues with users. Prompt engineering enhances the chatbot's conversational abilities and personality traits, fostering a more human-like interaction with patients. Ultimately, the injection of personality into the chatbot could potentially increase patient engagement. Future directions for research include investigating ways to improve chatbots' understanding of context and ensuring the accuracy of their outputs through fine-tuning with specialized medical data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
