The capabilities of the most recent language models have increased the interest in integrating them into real-world applications. However, the fact that these models generate plausible, yet incorrect text poses a constraint when considering their use in several domains. Healthcare is a prime example of a domain where text-generative trustworthiness is a hard requirement to safeguard patient well-being. In this paper, we present Physio, a chat-based application for physical rehabilitation. Physio is capable of making an initial diagnosis while citing reliable health sources to support the information provided. Furthermore, drawing upon external knowledge databases, Physio can recommend rehabilitation exercises and over-the-counter medication for symptom relief. By combining these features, Physio can leverage the power of generative models for language processing while also conditioning its response on dependable and verifiable sources. A live demo of Physio is available at https://physio.inesctec.pt.

翻译：摘要：最新语言模型的能力激发了将其整合到现实应用中的兴趣。然而，这些模型生成看似合理但实际错误的文本这一事实，限制了其在多个领域的应用。医疗保健是一个典型领域，其中文本生成的可靠性是保障患者健康的硬性要求。本文介绍了一款名为Physio的聊天式物理康复应用。Physio能够在引用可靠健康来源以支撑所提供信息的同时，进行初步诊断。此外，借助外部知识数据库，Physio可推荐康复锻炼和非处方药物以缓解症状。通过结合这些功能，Physio既能利用生成模型在语言处理上的优势，又能将其响应基于可靠且可验证的信息源。Physio的实时演示访问地址为：https://physio.inesctec.pt。