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Privacy preserving technology in ophthalmology.

Yahan Yang1, Xinwei Chen1, Haotian Lin1,2,3

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong.

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Privacy-preserving technologies are crucial for ophthalmic big data. While general methods like federated learning are emerging, specific techniques and ethical considerations require further development for better patient data protection.

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Data Privacy

Background:

  • Digitization of medical records increases accessibility and data sharing.
  • Ophthalmic data (periocular, iris, fundus images) contains sensitive biometric information.
  • Patient privacy is a critical concern in medical practice, especially in ophthalmology.

Purpose of the Study:

  • To review privacy-preserving technologies in ophthalmology.
  • To highlight the importance of protecting patient privacy in the era of big data.
  • To examine advancements and limitations in ophthalmic data privacy.

Main Methods:

  • Review of current literature on privacy-preserving technologies.
  • Analysis of general medical privacy techniques applied to ophthalmology.
  • Examination of specific ophthalmic privacy techniques and ethical considerations.

Main Results:

  • General privacy technologies like federated learning and blockchain are being adopted in ophthalmology.
  • Exploration of specific techniques, such as digital masks, remains limited.
  • Advancements in algorithm fairness and explainability are addressing ethical issues in ophthalmic big data.

Conclusions:

  • Ophthalmic patient privacy faces ongoing challenges requiring enhanced strategies.
  • Progress in privacy technology will improve healthcare, patient experience, and research.
  • Effective data sharing and scientific research depend on robust privacy protection.