Artificial Intelligence user interface preferences in radiology: A scoping review
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) in radiology is growing, but research on user interface preferences is limited. Future studies should involve more imaging professionals to guide AI development for better clinical integration.
Area Of Science
- Radiology
- Medical Imaging
- Artificial Intelligence
Background
- Artificial intelligence (AI) is increasingly utilized in radiology to support clinical processes amidst workforce shortages.
- The development of AI in radiology necessitates an understanding of user interface preferences for effective integration.
Purpose Of The Study
- To conduct a scoping review investigating existing literature on the preference of artificial intelligence (AI) interfaces within a radiology context.
- To identify current research trends and gaps in understanding user preferences for AI tools in medical imaging.
Main Methods
- A systematic scoping review was conducted using Arksey O'Malley's and Levac's framework.
- Searches were performed across four databases (MEDLINE Ovid, Scopus, Web of Science, Engineering Village) with inclusion criteria focused on radiological AI user interface preferences.
- Three researchers were involved in paper selection to ensure reliability.
Main Results
- Six papers met the inclusion criteria, employing diverse methodologies including observational studies, simulated user testing, and diagnostic accuracy studies.
- AI user interfaces were evaluated, with some preference for heatmap image overlays and highly detailed interfaces.
- Limited literature exists on AI user interfaces, with a lack of research on current interface preferences, both pre- and post-deployment.
Conclusions
- The current research landscape reveals a lack of standardized methods for assessing AI tool design and user preference in radiology.
- While some preferences emerged (e.g., simple, non-complicated designs), findings regarding ideal user interfaces are not clear.
- Further research is essential, focusing on end-user involvement, particularly radiographers, and establishing standardized outcome measures for AI interface evaluation.
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