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A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature.

Angela McCarthy1, Ives Valenzuela1, Royce W S Chen1

  • 1Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York.

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This review provides ophthalmologists with practical guidance on evaluating artificial intelligence (AI) research. It demystifies AI model design and offers strategies for assessing AI imaging models in publications.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Clinical Research Evaluation

Background:

  • Artificial intelligence (AI) is rapidly advancing ophthalmology, impacting diagnostics, treatment, and patient care.
  • A knowledge gap exists for ophthalmologists regarding AI technologies and their clinical integration.
  • Practical guidance is needed for clinicians to critically assess AI in research.

Purpose of the Study:

  • To bridge the gap in AI expertise for ophthalmologists.
  • To demystify artificial intelligence model design for clinical applications.
  • To provide practical recommendations for evaluating AI imaging models in ophthalmology research.

Main Methods:

  • Educational review synthesizing key considerations for AI paper evaluation.
  • Insights from an interdisciplinary team of ophthalmologists and AI experts.
  • Development of a structured framework based on expert discussions and AI research methodology.

Main Results:

  • A stepwise approach for evaluating AI models in ophthalmology.
  • Practical strategies for clinicians to assess AI research publications.
  • Broad recommendations applicable across ophthalmology and general medicine.

Conclusions:

  • Proactive engagement with AI empowers clinicians in healthcare innovation.
  • Prioritizing patient safety and quality of care is crucial during AI integration.
  • This guide equips ophthalmologists to navigate the evolving landscape of AI in medicine.