人工智能用于临床决策支持的照顾点成像:对诊断准确度,任务转移和可解释性的系统审查
Peter Wadie1, Bishoy Zakher2, Khalid Elgazzar1
1Department of Electrical, Computer, and Software Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe Street North, Oshawa, CA.
JMIR AI
|February 10, 2026
概括
人工智能 (AI) 在 point-of-care (POC) 图像中显示出高精度,并使非专业人员能够转移任务. 然而,患者结果和可解释性的证据差距阻碍了广泛采用.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 临床诊断 临床诊断 临床诊断
背景情况:
- 人工智能与护理点 (POC) 成像集成,在资源有限的环境中提供了扩展的诊断访问.
- 缺乏对各种POC成像模式的AI辅助临床决策支持的全面评估.
- 现有研究尚未充分评估可解释性实施或量化临床影响证据缺口.
研究的目的:
- 系统地审查和综合基于AI的临床决策支持系统使用POC成像的证据.
- 具体评估任务转移潜力,可解释的AI (XAI) 实施和临床结果证据.
- 识别未来研究和开发的关键证据缺口.
主要方法:
- 在PubMed,Scopus,IEEE Xplore和Web of Science (2018年1月 - 2025年11月) 进行系统的文献搜索.
- 包括评估POC成像AI/ML系统的研究与临床决策支持输出.
- 15个领域的数据提取,方法质量评估 (QUADAS-2) 和由于异质性的叙事综合.
主要成果:
- 20项研究 (78,296名患者) 在胸部X射线,超声波, fundus摄影和其他POC成像模式中评估AI.
- 人工智能显示出高诊断准确度 (中位灵敏度92%,特异性90.6%),并在65%的研究中实现了任务转移.
- 在可解释性 (75%的研究沉默) 和临床影响 (没有报告患者结果) 中发现了重大差距;70%的研究存在高偏差风险.
结论:
- 人工智能辅助的POC成像显示出诊断准确性和最小训练的任务转移的希望.
- 在患者的治疗结果,可解释性评估和多背景验证方面,仍然存在关键证据缺口.
- 在广泛采用之前,专注于患者结果和严格的XAI评估的实施研究至关重要.
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