NFRs in Medical Imaging
This work tackles the problem of AI adoption in hospitals for medical imaging by focusing on requirement engineering, but it is incremental as it applies existing NFR frameworks to a specific domain.
The study addressed the challenge of implementing AI in medical imaging by identifying key non-functional requirements (NFRs) important to stakeholders, finding that Efficiency, Accuracy, Interoperability, Reliability, Usability, Adaptability, and Fairness were critical, with Efficiency highlighted due to time pressures in diagnostic departments.
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.