用语音AI助手降低医疗成本，提升偏远地区患者监测效率。
Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery
- 基于大模型的语音助手实现持续患者监测与预防护理。
- 33名患者中70%接受AI监控，37%更偏好此方式。
- 适合关注医疗可及性与成本控制的管理者与技术开发者。
将语音驱动的AI代理融入医疗体系，有望解决数字健康服务中的经济与可及性鸿沟。本文探讨了大型语言模型（LLM）赋能的语音助手在增强预防性护理和持续患者监测方面的作用，尤其针对资源匮乏人群。基于IBM研究院、克利夫兰诊所基金会与莫尔黑斯医学院合作开发的Agent PULSE（患者理解与联络支持引擎）项目，我们构建了一个经济模型，证明在人力干预不具经济可行性的场景下，AI代理可提供低成本医疗服务。对33名炎症性肠病患者的试点研究显示，70%的患者接受了AI驱动的监测，其中37%更倾向于该模式。文中分析了实时对话处理、系统集成及隐私合规等技术挑战，并讨论了监管、偏见缓解与患者自主权等政策议题。研究发现，语音式AI代理不仅能提升医疗可扩展性与效率，还能增强患者参与度与可及性。对医疗管理者而言，我们的成本-效用分析揭示了常规监测任务的巨大潜在节省；对技术人员，则提供了以最大患者影响为导向的优化框架。通过克服现有局限并契合伦理与监管框架，语音类AI代理将成为实现公平、可持续数字医疗的关键入口。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。