The healthcare landscape is evolving, with patients seeking reliable information about their health conditions and available treatment options. Despite the abundance of information sources, the digital age overwhelms individuals with excess, often inaccurate information. Patients primarily trust medical professionals, highlighting the need for expert-endorsed health information. However, increased patient loads on experts has led to reduced communication time, impacting information sharing. To address this gap, we developed CataractBot, an experts-in-the-loop chatbot powered by LLMs, in collaboration with an eye hospital in India. CataractBot answers cataract surgery related questions instantly by querying a curated knowledge base and provides expert-verified responses asynchronously. It has multimodal and multilingual capabilities. In an in-the-wild deployment study with 55 participants, CataractBot proved valuable, providing anytime accessibility, saving time, accommodating diverse literacy levels, alleviating power differences, and adding a privacy layer between patients and doctors. Users reported that their trust in the system was established through expert verification. Broadly, our results could inform future work on designing expert-mediated LLM bots.

翻译：医疗健康领域正在经历变革，患者日益寻求关于自身健康状况及可用治疗方案的可靠信息。尽管信息来源丰富，数字时代却使个体淹没于过量且往往不准确的信息之中。患者主要信赖医疗专业人员，这凸显了专家认证健康信息的必要性。然而，专家接诊量的增加导致医患沟通时间减少，影响了信息传递。为弥补这一缺口，我们与印度一家眼科医院合作开发了CataractBot——一个基于大语言模型、采用专家参与式设计的聊天机器人。该系统通过查询经筛选的知识库即时回答白内障手术相关问题，并提供异步的专家验证回复。该机器人具备多模态与多语言处理能力。在一项包含55名参与者的真实场景部署研究中，CataractBot被证实具有重要价值：提供全天候可访问性、节省时间、适应不同文化程度用户、缓解权力差异，并在医患间构建隐私保护层。用户报告显示，专家验证机制是其建立系统信任的关键。总体而言，我们的研究成果可为未来设计专家介入式大语言模型机器人的工作提供参考。