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, UK.
Studies in health technology and informatics
|October 23, 2023
概括
这项研究检查了早期利益相关者与人工智能成像工具用于肺结节检测的经验. 调查结果突出了可变的绩效和采用挑战,强调需要进一步研究整合和组织因素.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 基于人工智能的临床决策支持系统正在诊断领域出现.
- 了解它们在临床实践中的整合是有限的.
- 研究了肺结节检测软件 (肺结节 - VLN).
研究的目的:
- 探索早期的利益相关者对Veye Lung Nodules (VLN) 人工智能工具的经验.
- 了解VLN如何与临床工作流程和组织实践集成.
- 确定影响放射学诊断AI采用和性能的因素.
主要方法:
- 与各种利益相关者 (临床医生,患者,决策者,学者) 进行了半结构化访谈和观察.
- 在早期采用者网站收集数据.
- 技术,人,组织和宏观环境因素 (TPOM) 框架用于编码.
- 完成了39次采访.
主要成果:
- 临床医生发现VLN易于使用,工作流程中断最小.
- 专家用户对系统的局限性进行了批判性评估和补偿.
- 患者对该工具的看法是积极的,但性能因地点和用例而异.
- 实施的挑战包括系统集成,数据保护,采用问题和成本.
结论:
- 人工智能工具的性能和使用受到工作流集成,组织因素和技术基础设施的影响.
- 观察到工具性能和使用的背景变化.
- 为了在临床环境中成功采用人工智能,需要对这些研究不足的因素进行进一步的研究.
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