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Artificial Intelligence user interface preferences in radiology: A scoping review.

Avneet Gill1, Clare Rainey1, Laura McLaughlin1

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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.

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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.