在放射学中实施人工智能决策支持系统:使用非采用放弃扩大,扩散和可持续性 (NASSS) 框架进行前性定性评估研究
Sundresan Naicker1, Paul Schmidt2, Bruce Shar2
1Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Australia.
Journal of medical Internet research
|January 28, 2026
概括
在放射学中实施人工智能 (AI) 需要在技术方面同时解决组织和文化因素. 人工智能决策支持的可持续采用取决于临床医生的参与,反,以及解决绩效和沟通问题.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 医疗信息学 医疗信息学
背景情况:
- 数字健康技术,包括AI临床决策支持系统,越来越多地被整合到医学成像实践中.
- 虽然人工智能显示出临床有效性,但其长期实施和对放射科医生的工作流程的影响尚不清楚.
研究的目的:
- 在不同阶段的放射学中对人工智能决策支持工具的现实世界实施进行定性评估.
- 通过NASSS框架识别影响人工智能采用和可持续性的因素.
- 为有效和安全的AI整合在公共医院的战略提供信息.
主要方法:
- 一项前性定性研究,涉及43个半结构面试与放射科医生和放射学家.
- 采访是在第三级医院的实施前,期间和后阶段进行的.
- 不采用,放弃,扩大,扩散和可持续性 (NASSS) 框架指导了实施因素的确定.
主要成果:
- 组织障碍很早就很突出,转向技术问题,如在实施后的准确性和信息过载.
- 随着时间的推移,启用者增加了,一些临床医生使用AI作为安全检查.
- 信任和采用受到不一致的表现,沟通不良和医疗法律不确定性所阻碍.
结论:
- 人工智能在放射学中的实施是一个复杂的社会技术过程,涉及组织和文化元素.
- 可持续的AI采用需要解决用户体验,增强沟通和培训,并促进反循环.
- 纳斯斯框架为有效的AI集成提供了对动态交互的洞察力.
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