人在医疗保健中的循环人工智能:应用,结果和实施挑战
David B Olawade1, Shamiul Bashir Plabon2, Adeyinka Ojo3
1Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Public Health, York St John University, London E14 2BA, United Kingdom.
International journal of medical informatics
|February 25, 2026
概括
人在循环中的人工智能 (AI) 通过将人类专业知识与机器智能相结合来提高医疗保健. 与完全自动化系统相比,这种协作方法可以提高诊断准确性和患者安全性.
科学领域:
- 医疗保健人工智能的人工智能
- 人与计算机的交互
- 临床信息学 临床信息学
背景情况:
- 人工智能 (AI) 在医疗保健中的整合引发了对问责制,可解释性和安全性的担忧.
- 人类监督对于在临床环境中道德和安全地部署人工智能至关重要.
- 人类在循环中 (HITL) 人工智能促进了人类专业知识和机器智能之间的合作.
研究的目的:
- 综合关于HITL AI在医疗保健提供和研究中的证据.
- 检查HITL AI实施框架,临床结果和比较优势.
- 确定HITL AI在医疗保健中的挑战和未来方向.
主要方法:
- 从2018-2025年对研究进行了全面的叙事审查.
- 在PubMed,Scopus,Web of Science和IEEE Xplore数据库中进行了搜索.
- 对数据进行主题综合,以确定模式,框架和结果.
主要成果:
- 在诊断成像,临床决策支持,患者监测,药物发现和研究方面,HITL AI显示出前景.
- 希特尔AI提高了诊断准确度,减少了医疗错误,并增强了临床医生的信任.
- 实施需要EHR的互操作性,明确的责任,适应性培训和安全的加密;挑战包括工作流集成和监管差距.
结论:
- 在医疗保健领域,HITL AI优化了人机协作.
- 未来的方向包括后量子密码学,人工智能驱动的自适应架构和多中心可扩展性.
- 解决适应性系统的监管缺口对于HITL AI的发展至关重要.
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