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Related Experiment Video

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Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats
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Planning an artificial intelligence diabetic retinopathy screening program: a human-centered design approach.

Angelica C Scanzera1, Cameron Beversluis2, Archit V Potharazu2

  • 1Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States.

Frontiers in Medicine
|July 24, 2023
PubMed
Summary
This summary is machine-generated.

Implementing artificial intelligence (AI) for diabetic retinopathy (DR) screening in primary care can prevent vision loss. This study outlines a human-centered design strategy to integrate AI into clinical practice, overcoming adoption barriers.

Keywords:
artificial intelligencediabetic retinopathyhuman-centered designimplementationscreening

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Area of Science:

  • Ophthalmology
  • Public Health
  • Health Informatics

Background:

  • Diabetic retinopathy (DR) is a primary cause of vision loss globally.
  • Early detection and treatment of DR are crucial for preventing severe visual impairment.
  • Despite proven effectiveness, artificial intelligence (AI) for DR screening faces slow clinical adoption.

Purpose of the Study:

  • To present a strategy for integrating AI-based DR screening into primary care settings.
  • To detail a human-centered design approach for AI implementation in healthcare.
  • To share findings from a community case study on AI adoption in DR care.

Main Methods:

  • Adapted the United Kingdom Design Council's Double-Diamond model for care delivery strategy.
  • Utilized human-centered design principles to address implementation barriers and facilitators.
  • Engaged key stakeholders to develop protocols, educational materials, and workflows.

Main Results:

  • Developed a comprehensive strategy for integrating AI-based DR screening into primary care.
  • Identified context-specific barriers and facilitators for AI adoption.
  • Created practical resources including protocols, educational documents, and workflows.

Conclusions:

  • A human-centered design approach can facilitate the integration of AI screening for diabetic retinopathy into primary care.
  • Addressing implementation barriers through stakeholder input is key to successful AI adoption.
  • This strategy offers a framework for improving DR care delivery and preventing vision loss.