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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.

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Artificial Intelligence (AI) enhances eye care by improving diagnosis and management of eye diseases. While AI shows great promise, challenges in implementation and ethics need addressing for wider use in ophthalmology.

Keywords:
artificial intelligence (ai)artificial intelligence in healthcaredeep learning artificial intelligencemachine learningophthalmology

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Area of Science:

  • Ophthalmology and Medical Artificial Intelligence (AI)
  • Application of AI in diagnosing and managing ocular conditions

Background:

  • AI is revolutionizing medicine, particularly in ophthalmology, for diagnosing, managing, and treating eye diseases.
  • The review focuses on AI's role in common conditions like diabetic retinopathy, cataract, glaucoma, and age-related macular degeneration.

Purpose of the Study:

  • To provide a comprehensive literature review on the application and utilization of AI in ophthalmology.
  • To assess the effectiveness and limitations of current AI systems in ophthalmic disease detection and management.

Main Methods:

  • A systematic search of PubMed and Google Scholar databases was conducted.
  • 102 studies were included, covering diverse ophthalmology subspecialties.
  • Focus on deep learning (DL) and machine learning (ML) algorithms for analyzing ophthalmic data.

Main Results:

  • AI, particularly DL and ML, demonstrates high accuracy in diagnosing and classifying eye diseases from retinal images and OCT scans.
  • AI systems show comparable accuracy to clinical experts, offering reduced subjectivity and time.
  • AI applications extend to retina, glaucoma, cornea, pediatric ophthalmology, neuro-ophthalmology, and ocular emergencies.

Conclusions:

  • AI is transforming screening, diagnosis, and management in ophthalmology, with significant potential for personalized treatment plans.
  • Challenges including regulatory approval, data quality, standardization, and ethical concerns hinder widespread AI implementation.
  • Future research must address these barriers to optimize AI for enhanced patient care in ophthalmology.