The healthcare landscape is evolving, with patients seeking more reliable information about their health conditions, treatment options, and potential risks. Despite the abundance of information sources, the digital age overwhelms individuals with excess, often inaccurate information. Patients primarily trust doctors and hospital staff, highlighting the need for expert-endorsed health information. However, the pressure on experts has led to reduced communication time, impacting information sharing. To address this gap, we propose CataractBot, an experts-in-the-loop chatbot powered by large language models (LLMs). Developed in collaboration with a tertiary eye hospital in India, CataractBot answers cataract surgery related questions instantly by querying a curated knowledge base, and provides expert-verified responses asynchronously. CataractBot features multimodal support and multilingual capabilities. In an in-the-wild deployment study with 49 participants, CataractBot proved valuable, providing anytime accessibility, saving time, and accommodating diverse literacy levels. Trust was established through expert verification. Broadly, our results could inform future work on designing expert-mediated LLM bots.

翻译：医疗健康领域正持续演变，患者日益寻求关于自身健康状况、治疗方案及潜在风险的可靠信息。尽管信息源层出不穷，数字时代却使人们淹没在过量且往往失准的信息中。患者主要信任医生与医院工作人员，凸显了经专家认可的健康信息的必要性。然而，专家面临的压力导致医患沟通时间缩短，影响了信息传递效率。为弥补这一缺口，我们提出白内障智能助手——一种由大语言模型驱动的专家在环式聊天机器人。该机器人通过与印度某三级眼科医院合作开发，能够即时查询精选知识库以解答白内障手术相关问题，并通过异步方式提供经专家验证的回复。白内障智能助手具备多模态支持与多语言能力。在一项涉及49名参与者的实地部署研究中，该机器人展现出显著价值：实现全天候可及性、节省时间，并适应不同文化水平的用户需求。通过专家验证机制建立了用户信任。总体而言，我们的研究成果可为未来设计专家中介型大语言模型机器人提供参考。