将基于人工智能的诊断决策支持系统集成到放射学环境中的早期经验:一种定性研究
Nuša Farič1, Sue Hinder1, Robin Williams2
1Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
Journal of the American Medical Informatics Association : JAMIA
|September 25, 2023
概括
早期使用基于AI的Veye Lung Nodules (VLN) 软件来检测肺结节的早期使用显示出可变的性能. 社会组织因素对诊断AI工具产生了重大影响.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 基于人工智能的临床决策支持系统正在出现用于诊断.
- 我们对这些系统如何融入临床实践的理解有限.
- 在CT扫描中检测肺结节是AI应用的一个关键领域.
研究的目的:
- 探索早期利益相关者使用人工智能工具 (Veye Lung Nodules - VLN) 进行肺结节分析的经验.
- 了解诊断AI在临床工作流程中的整合.
- 确定影响放射学AI性能和采用AI的因素.
主要方法:
- 与各种利益相关者 (临床医生,患者,决策者,学者) 进行了半结构化的采访和观察.
- 在VLN软件的早期采用者网站收集数据.
- 技术,人,组织和宏观环境因素框架用于数据编码.
主要成果:
- 临床医生发现VLN易于使用,工作流程中断最小.
- 专家用户批判性地评估和补偿AI的局限性,与新手不同.
- 工具性能因网站,用例,工作流集成和技术因素而有所不同.
- 患者对人工智能工具的看法是积极的.
结论:
- 社会组织因素极大地影响诊断AI性能,需要进一步调查.
- 成功的AI集成取决于工作流程,人-AI交互和组织环境.
- 解决系统集成和成本等实施挑战对于持续采用人工智能至关重要.
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