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Artificial Intelligence in Oculoplastics: A Review.

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Artificial intelligence (AI) shows promise in diagnosing and detecting various oculoplastic conditions, including eyelid ptosis and periocular cancers. Challenges remain in data privacy, ethics, and real-world validation of AI algorithms.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Oculoplastics

Background:

  • Artificial intelligence (AI) is increasingly used for disease analysis in ophthalmology.
  • Previous AI applications have focused on anterior and posterior segment diseases.
  • The role of AI in oculoplastic conditions requires further investigation.

Purpose of the Study:

  • To review the existing literature on the application of AI in oculoplastic diseases.
  • To identify current AI uses and potential future directions in oculoplastics.

Main Methods:

  • A comprehensive literature search was performed on Pubmed and Embase.
  • Keywords included AI combined with various oculoplastic terms (e.g., ptosis, eyelid cancer, thyroid eye disease).
  • AI large language models were excluded from the review.

Main Results:

  • The review encompassed studies on eyelid and brow ptosis (19), periocular and intraocular cancer (16), orbital trauma and tumors (9), inflammatory/infectious orbital conditions (17), and thyroid eye disease (30).
  • AI applications focused on diagnosis, severity assessment, and outcome prediction for these conditions.
  • Specific AI uses include diagnosing ptosis, detecting eyelid cancer from images, and monitoring thyroid eye disease.

Conclusions:

  • AI demonstrates expanding potential in oculoplastics, particularly for diagnosing conditions like ptosis and eyelid cancer.
  • AI is also being explored for orbital tumors, radiographic image evaluation, and thyroid eye disease management.
  • Significant challenges include data privacy, ethical concerns, data standardization, and the need for ongoing clinical validation of AI tools.