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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives.

Kai Jin1, Juan Ye1

  • 1Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.

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|October 17, 2023
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Summary

Artificial intelligence (AI) in ophthalmology aids disease diagnosis, but clinical use is limited by trust and explainability issues. This review explores AI applications, challenges, and future directions in eye care.

Keywords:
Age-related macular degenerationArtificial intelligenceDeep learningDiabetic retinopathyGlaucomaOphthalmology

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Deep Learning

Background:

  • Ophthalmology was an early adopter of artificial intelligence (AI) in medicine.
  • Digitized ocular images and large datasets fuel interest in deep learning (DL) for eye diseases.

Purpose of the Study:

  • To review AI applications in ophthalmology.
  • To highlight clinical considerations for AI adoption.
  • To discuss challenges and future directions for AI in eye care.

Main Methods:

  • Literature review of AI applications in ophthalmology.
  • Analysis of current AI systems for ophthalmic diseases.
  • Examination of barriers to clinical AI implementation.

Main Results:

  • AI is primarily used for diagnosing ophthalmic diseases like diabetic retinopathy, glaucoma, and AMD.
  • Most AI systems remain experimental due to security, privacy, trust, and explainability concerns.
  • Limited AI tools have achieved widespread clinical application.

Conclusions:

  • AI offers significant potential for improving ophthalmic disease diagnosis and decision-making.
  • Addressing challenges like trust and explainability is crucial for broader AI adoption.
  • Future research should focus on overcoming barriers to clinical implementation and exploring novel AI applications.