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Big data requirements for artificial intelligence.

Sophia Y Wang1, Suzann Pershing1, Aaron Y Lee2

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Area of Science:

  • Ophthalmology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Healthcare data, including ophthalmology, is rapidly expanding in volume and variety.
  • Electronic Health Records (EHRs) and data exchange standards (e.g., DICOM, FHIR) facilitate big data growth.
  • Cloud computing provides robust infrastructure for big data and AI applications in healthcare.

Purpose of the Study:

  • To review the evolution and current state of big data and artificial intelligence (AI) in ophthalmology.
  • To outline future directions for AI development in ophthalmology.

Main Methods:

  • Review of current literature on big data and AI in ophthalmology.
  • Analysis of technological advancements and data management practices.
  • Identification of challenges and requirements for future AI implementation.

Main Results:

  • Ophthalmology is a leader in AI research due to its high-volume imaging and structured data.
  • Key needs include consensus on data labeling for supervised learning, data sharing, and AI model architecture standards.
  • Open application program interfaces (APIs) are crucial for accessing AI models.

Conclusions:

  • Reproducible science and open innovation are essential for advancing AI in ophthalmology.
  • Standardization of data labels, data sharing, model architectures, and accessible APIs will support clinical AI adoption.
  • Continued development is needed to fully leverage big data and AI for future ophthalmology applications.