绘制和总结人工智能系统用于自动化病史记录和分离的研究:范围审查
Elin Siira1, Hanna Johansson1, Jens Nygren1
1School of Health and Welfare, Halmstad University, Halmstad, Sweden.
Journal of medical Internet research
|February 6, 2025
概括
本范围审查检查了人工智能 (AI) 系统的病史采集和选,发现有关利益相关者的观点和现实世界的实施有限的研究. 需要进一步的研究来弥合这些差距,以便有效地将AI整合到医疗保健中.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床工作流的优化 临床工作流优化
背景情况:
- 人工智能 (AI) 提供了自动化病史记录和分拣的潜力,以提高医疗保健的效率.
- 尽管人工智能研究很有前途,但在日常实践中有限的整合需要了解实施促进者和障碍.
- 识别利益相关者的观点对于在医疗保健环境中成功部署人工智能至关重要.
研究的目的:
- 系统地绘制和总结关于人工智能系统的实证研究,用于自动化病史记录和分类.
- 确定人工智能医疗应用中的当前趋势,实施挑战和利益相关者的观点.
- 为AI在医学史和诊断中的研究环境提供全面的概述.
主要方法:
- 根据阿克西和奥马利框架和PRISMA-ScR指南进行范围审查.
- 在五个主要数据库 (PubMed,CINAHL,PsycINFO,Scopus,Web of Science) 进行全面的文献搜索.
- 系统选和综合86个符合条件的AI在医疗保健环境中的研究出版物.
主要成果:
- 大多数研究 (73%) 发表于2020-2022年期间,重点关注紧急护理 (37%).
- 混合人工智能模型 (79%) 占主导地位,预测 (47%) 和识别 (42%) 是常见的任务;大多数研究重点是开发和验证 (88%).
- 在研究患者 (1%) 和医疗保健专业人员 (2%) 的观点以及现实世界的临床验证 (7%) 中存在重大差距.
结论:
- 目前,医疗史采集和分类方面的人工智能研究重点是系统开发,而不是实施和利益相关者参与.
- 关于患者和医疗保健专业人士的观点,存在有限的经验证据,阻碍了成功的AI集成.
- 解决有关利益相关者的观点和现实世界的验证所识别的文献差距对于推动医疗保健中人工智能采用至关重要.
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