评估基于人工智能的语音识别用于临床文档的性能:系统性审查
Joel Jia Wei Ng1, Eugene Wang1, Xinyan Zhou1
1NUS Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
BMC medical informatics and decision making
|July 2, 2025
概括
人工智能转录工具对临床文档有希望,但准确性差异很大. 为了可靠地融入医疗保健工作流程,需要进一步开发.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 临床文档对于患者护理和法律遵守至关重要.
- 传统的方法效率低下,并导致临床医生疲劳.
- 人工智能转录,使用自动语音识别 (ASR) 和自然语言处理 (NLP),提供了自动化潜力.
研究的目的:
- 在临床环境中系统审查评估AI转录工具的研究.
- 评估这些系统的性能,准确性和效率.
- 确定人工智能驱动的临床文档的挑战和改进领域.
主要方法:
- 在MEDLINE,Embase和Cochrane图书馆进行全面的文献搜索,截至2025年2月16日.
- 包括临床医生使用的AI转录软件研究,报告准确性,时间效率和用户满意度.
- 使用QUADAS-2进行系统的数据提取和质量评估,然后进行叙事综合.
主要成果:
- 包括29项研究,其中Word错误率 (从0.087到>50%) 和F1得分 (从0.416到0.856) 的差异很大.
- 一些研究报告了节省时间和改善笔记完整性,而另一些研究则指出编辑需求增加和特殊术语或口音的错误.
- 基于大型语言模型 (LLM) 的系统提供了总结,但需要人类监督以确保安全.
结论:
- 人工智能转录系统具有潜力,但在准确性,适应性和工作流集成方面面临挑战.
- 特定领域的培训,实时纠错和EHR互操作性对于采用至关重要.
- 未来的研究应该专注于具有LLM总结能力的高级"数字书记".
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