医疗影像AI落地难？关键在非功能需求的管理。
NFRs in Medical Imaging
- 通过访谈丹麦医院人员，识别出医疗影像AI的关键非功能需求
- 效率、准确率、可互操作性等7类需求被列为重要指标
- 强调效率对缩短每例扫描时间至关重要，适合医工交叉研究者
诊断影像部门面临日益增长的工作量和合格人力短缺的压力。尽管人工智能在医学影像应用中展现出巨大潜力，但极少有诊断影像模型获得医院批准使用，更少被实际部署。软件项目失败最常见的原因是需求工程不佳，尤其是非功能性需求（NFRs）处理不当。研究发现，机器学习从业者在应对NFR方面存在困难，亟需将现有NFR框架适配于机器学习与AI软件系统。本研究采用定性方法，与关键利益相关者互动，识别出医疗影像应用中重要的NFR类型。研究基于丹麦单一医院开展，结果显示：效率、准确性、互操作性、可靠性、可用性、适应性和公平性七类非功能需求对利益相关者至关重要。其中，效率尤为关键，因影像科希望尽可能缩短每例扫描的处理时间。
原文摘要 · Abstract (English)
The diagnostic imaging departments are under great pressure due to a growing workload. The number of required scans is growing and there is a shortage of qualified labor. AI solutions for medical imaging applications have shown great potential. However, very few diagnostic imaging models have been approved for hospital use and even fewer are being implemented at the hospitals. The most common reason why software projects fail is poor requirement engineering, especially non-functional requirements (NFRs) can be detrimental to a project. Research shows that machine learning professionals struggle to work with NFRs and that there is a need to adapt NFR frameworks to machine learning, AI-based, software. This study uses qualitative methods to interact with key stakeholders to identify which types of NFRs are important for medical imaging applications. The study was done on a single Danish hospital and found that NFRs of type Efficiency, Accuracy, Interoperability, Reliability, Usability, Adaptability, and Fairness were important to the stakeholders. Especially Efficiency since the diagnostic imaging department is trying to spend as little time as possible on each scan.
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