Zhan Jiawen, Moore Dominic, Lu Yuanzhe, Abbasi Hamid
Machine Learning, Inspired Spine Health, Burnsville, USA.
Spine Surgery, Inspired Spine Health, Burnsville, USA.
Cureus. 2025 Mar 26;17(3):e81243. doi: 10.7759/cureus.81243. eCollection 2025 Mar.
Medical documentation is a major part of delivering healthcare worldwide and is gaining more importance in developing countries as well. The global spread of multilingual communities in medical documentation poses unique challenges, particularly regarding maintaining accuracy and consistency across diverse languages. Inspired Spine Smart Universal Resource Identifier (SURI), an adaptive artificial intelligence (AI) framework, addresses these challenges by transforming multilingual speech into structured medical reports. Utilizing state-of-the-art automatic speech recognition (ASR) and natural language processing (NLP) technologies, SURI converts doctor-patient dialogues into detailed clinical documentation. This paper presents SURI's development, focusing on its multilingual capabilities, effective report generation, and continuous improvement through real-time feedback. Our evaluation indicates a 60% reduction in documentation errors and a 70% decrease in time spent on medical reporting compared to traditional methods. SURI not only provides a practical solution to a pressing issue in healthcare but also sets a benchmark for integrating AI into medical communication workflows.
医学文档是全球医疗保健服务的重要组成部分，在发展中国家也日益重要。医学文档中多语言群体的全球传播带来了独特的挑战，尤其是在跨不同语言保持准确性和一致性方面。Inspired Spine智能通用资源标识符（SURI）是一种自适应人工智能（AI）框架，通过将多语言语音转换为结构化医学报告来应对这些挑战。利用最先进的自动语音识别（ASR）和自然语言处理（NLP）技术，SURI将医患对话转换为详细的临床文档。本文介绍了SURI的开发，重点关注其多语言能力、有效的报告生成以及通过实时反馈进行的持续改进。我们的评估表明，与传统方法相比，文档错误减少了60%，医学报告时间减少了70%。SURI不仅为医疗保健中的紧迫问题提供了切实可行的解决方案，还为将人工智能集成到医疗通信工作流程中树立了标杆。
