基于人工智能和电子健康记录的初级保健诊断预测模型:系统审查
Liesbeth Hunik1, Asma Chaabouni1, Twan van Laarhoven2
1Department of Primary and Community Care, Research Institute for Medical Innovation, Radboudumc, Geert Grooteplein Zuid 21, Nijmegen, 6525 GA, The Netherlands, 31 243618181.
JMIR medical informatics
|August 22, 2025
概括
使用电子健康记录的人工智能模型对初级保健诊断有希望,但需要进一步开发. 目前大多数人工智能模型具有偏差的高风险,尚未准备好用于临床应用.
科学领域:
- 医疗信息学
- 医疗保健中的人工智能
- 主要护理研究
背景情况:
- 人工智能 (AI) 通过利用电子健康记录 (EHR) 数据来提高初级保健 (PC) 的诊断准确性.
- 尽管AI具有潜力,但使用PC EHR数据对基于AI的诊断预测模型的系统评估仍然缺乏.
- 现有研究已经探索了基于电子健康记录数据的各种预测模型,但需要进行全面的审查.
研究的目的:
- 系统评估使用PC EHR数据开发的基于AI的诊断预测模型.
- 评估这些人工智能模型的内容,偏差风险和适用性.
- 发现这些工具目前的研究和临床准备不足.
主要方法:
- 根据PRISMA指南进行了系统审查.
- 通过主要数据库 (MEDLINE,Embase,科学网,Cochrane) 进行了搜索.
- 包括使用PC EHR数据开发或验证AI诊断预测模型的研究;使用PROBAST评估偏差风险和适用性.
主要成果:
- 在10657份记录中, 选出了15篇论文,
- 只有两项研究在PC环境中对模型进行了外部验证;13项研究开发了模型.
- 在60%的研究中发现偏差的高风险,由于报告不足,在67%的研究中应用不清楚.
结论:
- 大多数基于人工智能的诊断预测模型专注于单一的慢性疾病,并且缺乏初级保健机构的强有力的外部验证.
- 发现了显著的方法限制和高偏差风险,阻碍了临床实施.
- 目前的人工智能诊断预测模型尚未充分开发或验证,无法在初级保健中常规使用.
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