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Comparing code-free and bespoke deep learning approaches in ophthalmology.

Carolyn Yu Tung WongCiara O'ByrnePriyal TaribagilTiming LiuFares AntakiPearse Andrew Keane
Published in: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie (2024)
For clinicians without DL expertise and easy access to AI experts, CFDL allows the prototyping of novel clinical AI systems. CFDL models concert with bespoke models, depending on the task at hand. A multidimensional, weighted evaluation of the factors involved in the implementation of those models for a designated task is warranted.
Keyphrases
  • artificial intelligence
  • deep learning
  • healthcare
  • primary care
  • magnetic resonance
  • palliative care
  • machine learning
  • quality improvement
  • computed tomography
  • contrast enhanced
  • psychometric properties