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 develop 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：医疗健康领域正在不断发展，患者越来越需要获取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，但数字时代使个体淹没在过量且往往不准确的信息中。患者主要信任医疗专业人员，这凸显了对专家认可的健康信息的需求。然而，专家接诊量的增加导致沟通时间减少，影响了信息共享。为弥补这一缺口，我们与印度一家眼科医院合作，开发了CataractBot——一种基于大语言模型、专家参与式的聊天机器人。CataractBot通过查询精心构建的知识库即时回答白内障手术相关问题，并提供专家异步验证的回复。该系统具备多模态与多语言能力。在一项包含55名参与者的真实场景部署研究中，CataractBot被证明具有重要价值：提供全天候可访问性、节省时间、适应不同文化水平、缓解权力差异，并在医患之间增加了隐私保护层。用户报告称，他们对系统的信任是通过专家验证建立的。总体而言，我们的研究结果可为未来设计专家介导的大语言模型机器人提供参考。