Title: Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery Show Chinese title

Title: 基于语音的AI代理：填补数字健康交付中的经济缺口

Abstract: The integration of voice-based AI agents in healthcare presents a transformative opportunity to bridge economic and accessibility gaps in digital health delivery. This paper explores the role of large language model (LLM)-powered voice assistants in enhancing preventive care and continuous patient monitoring, particularly in underserved populations. Drawing insights from the development and pilot study of Agent PULSE (Patient Understanding and Liaison Support Engine) -- a collaborative initiative between IBM Research, Cleveland Clinic Foundation, and Morehouse School of Medicine -- we present an economic model demonstrating how AI agents can provide cost-effective healthcare services where human intervention is economically unfeasible. Our pilot study with 33 inflammatory bowel disease patients revealed that 70\% expressed acceptance of AI-driven monitoring, with 37\% preferring it over traditional modalities. Technical challenges, including real-time conversational AI processing, integration with healthcare systems, and privacy compliance, are analyzed alongside policy considerations surrounding regulation, bias mitigation, and patient autonomy. Our findings suggest that AI-driven voice agents not only enhance healthcare scalability and efficiency but also improve patient engagement and accessibility. For healthcare executives, our cost-utility analysis demonstrates huge potential savings for routine monitoring tasks, while technologists can leverage our framework to prioritize improvements yielding the highest patient impact. By addressing current limitations and aligning AI development with ethical and regulatory frameworks, voice-based AI agents can serve as a critical entry point for equitable, sustainable digital healthcare solutions. Show Chinese abstract

Abstract: 语音驱动的人工智能代理在医疗保健中的整合为弥合数字健康交付中的经济和可及性差距提供了变革性的机会。 本文探讨了大型语言模型（LLM）驱动的语音助手在增强预防护理和持续患者监测中的作用，特别是在资源不足的人群中。 基于Agent PULSE（Patient Understanding and Liaison Support Engine）的开发和试点研究——这是IBM研究院、克利夫兰诊所基金会和莫尔豪斯医学院之间的合作项目——我们提出了一个经济模型，展示了人工智能代理如何在人类干预在经济上不可行的情况下提供成本效益高的医疗服务。 我们的33名炎症性肠病患者的试点研究表明，70%的患者表示接受人工智能驱动的监测，其中37%更倾向于这种方式而不是传统方法。 技术挑战，包括实时对话人工智能处理、与医疗系统集成以及隐私合规性，以及监管、偏见缓解和患者自主权相关的政策考虑因素都被进行了分析。 我们的研究结果表明，基于语音的人工智能代理不仅提高了医疗保健的可扩展性和效率，还改善了患者参与度和可及性。 对于医疗管理人员，我们的成本效用分析显示了在常规监测任务中巨大的潜在节省，而技术人员可以利用我们的框架来优先改进产生最大患者影响的方面。 通过解决当前的局限性并使人工智能的发展与伦理和监管框架保持一致，基于语音的人工智能代理可以成为实现公平、可持续的数字医疗解决方案的关键切入点。