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Ocular imaging and AI offer rapid eye disease diagnosis. However, interoperability challenges hinder widespread clinical adoption, especially in smaller eye clinics, impacting patient care and collaboration.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Office-based ocular imaging is standard for assessing eye structure and function.
  • Advances in imaging and AI present opportunities for rapid eye disease diagnosis.
  • Current clinical workflows often lack integration, particularly in smaller practices.

Purpose of the Study:

  • To describe the current state of ocular imaging.
  • To identify challenges in interoperability, reporting, and clinical workflow.
  • To highlight barriers to optimizing patient care in eye care settings.

Main Methods:

  • Review of current practices in office-based ocular imaging.
  • Analysis of digital workflow adoption in large institutions versus smaller clinics.
  • Examination of interoperability issues in healthcare, specifically within eye care.

Main Results:

  • Large institutions leverage integrated digital workflows (EHR, imaging archives, diagnostic viewers).
  • Smaller and independent eye clinics lag in adopting these advanced digital workflows.
  • Fragmented interoperability practices impede seamless data sharing and collaboration among healthcare providers.

Conclusions:

  • Interoperability, reporting, and workflow inefficiencies are key challenges in ocular imaging.
  • Addressing these issues is crucial for improving clinical practice and patient outcomes in eye care.
  • Enhanced collaboration is essential, given the scarcity of specialized eye care centers.