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[Artificial intelligence based on images in ophthalmology].

L L Xu1, Z Yang1, B Tian2

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[Zhonghua Yan Ke Za Zhi] Chinese Journal of Ophthalmology
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Summary
This summary is machine-generated.

Artificial intelligence (AI), particularly deep learning, is revolutionizing ophthalmology by reducing clinician workload. This review explores AI

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Artificial intelligence (AI) applications are increasingly prevalent in ophthalmology.
  • Machine learning, a subset of AI, and its deep learning algorithms are key drivers of this advancement.
  • AI integration aims to significantly alleviate the workload of ophthalmologists.

Purpose of the Study:

  • To summarize current applications of AI in ophthalmology.
  • To discuss the limitations and future directions of AI in ophthalmic practice.
  • To provide a reference for clinical implementation of AI tools.

Main Methods:

  • Literature review of AI applications in ophthalmology.
  • Analysis of machine learning and deep learning algorithms in ophthalmic research.
  • Discussion of current challenges and future prospects.

Main Results:

  • AI demonstrates significant utility across various ophthalmic subspecialties.
  • Deep learning algorithms show particular promise in image analysis and diagnosis.
  • Existing AI applications are effective in reducing ophthalmologists' workload.

Conclusions:

  • AI, especially deep learning, offers substantial benefits for ophthalmology.
  • Further research and development are needed to address AI's current inadequacies.
  • AI holds considerable potential to transform future clinical ophthalmology practices.