Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study

被引:11
作者
Faric, Nusa [1 ]
Hinder, Sue [1 ]
Williams, Robin [2 ]
Ramaesh, Rishi [3 ]
Bernabeu, Miguel O. [1 ,4 ]
van Beek, Edwin [5 ]
Cresswell, Kathrin [1 ]
机构
[1] Univ Edinburgh, Usher Inst, Old Med Sch,Teviot Pl, Edinburgh EH8 9AG, Scotland
[2] Univ Edinburgh, Inst Study Sci Technol & Innovat, Edinburgh, Scotland
[3] Royal Infirm Hosp, Dept Radiol, Edinburgh, Scotland
[4] Univ Edinburgh, Bayes Ctr, Edinburgh, Scotland
[5] Univ Edinburgh, Ctr Cardiovasc Sci, Edinburgh Imaging & Neurosci, Edinburgh, Scotland
关键词
artificial intelligence; radiology; clinical decision support; diagnostic;
D O I
10.1093/jamia/ocad191
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Objectives Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest.Materials and methods We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework.Results We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs.Discussion Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure.Conclusion The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.
引用
收藏
页码:24 / 34
页数:11
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