Protecting Data Privacy in the Age of AI-Enabled Ophthalmology
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Rigorous verification and validation mechanisms are particularly significant in precision-critical fields like ophthalmology.
“They advocate for rigorous verification and validation mechanisms, which gain particular significance in precision-critical fields like ophthalmology, as noted in [30].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [30]
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References 47 · Cited by 92
Research works printed but not linked: 4
- [40]Bonawitz K, Eichner H, Grieskamp W, et al. Towards federated learning at scale: system design. Available at: https://arxiv.org/pdf/1902.01046.pdf. Accessed June 22, 2020. · arXiv
- [42]Geyer RC, Klein T, Nabi M Differentially private federated learning: a client level perspective. Available at: https://arxiv.org/pdf/1712.07557.pdf. Accessed June 22, 2020. · arXiv
- [43]Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Ghahramani Z Welling M Cortes C Lawrence ND Weinberger KQ, eds. Advances in Neural Information Processing Systems 27. Red Hook, NY: Curran Associates; 2014: 2672– 2680.
- [48]Fredrikson M, Jha S, Ristenpart T Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. CCS ’15. New York, NY: Association for Computing Machinery; 2015: 1322– 1333.
- Authors
- Tom, E., Keane, P. A., Blazes, M., Pasquale, L. R., Chiang, M. F., Lee, A. Y., Lee, C. S., & and AAO Artificial Intelligence Task Force
- Affiliations
- University of Washington (US)
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Icahn School of Medicine at Mount Sinai (US)
- Oregon Health & Science University (US)
- Corresponding author
- Lee, Aaron Y.
- ORCID
- Venue
- Translational Vision Science & Technology
- Published
- 2020-07-06
- Volume
- 9
- Issue
- 2
- Pages
- 36
- ISSN
- 2164-2591 (ISSN-L)
- PMC
- PMC7424948
- OpenAlex
- W3038901217
- Cited by
- 92
- References
- 47
- Citations by year
- 2026: 11 · 2025: 31 · 2024: 15 · 2023: 11 · 2022: 14 · 2021: 10
- FWCI
- 2.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Translational Vision Science & Technology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute for Health and Care Research (CS-2014-14-023, CL-2010-18-004)
- Academy of Medical Sciences (AMS-SGCL6-Keane)
- National Institute on Aging (R01AG060942)
- National Eye Institute (R01EY019474, R01EY015473, K23EY029246, K12EY027720, P30EY010572)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- data privacy, artificial intelligence, ophthalmology, patient data protection
- MeSH terms
- Machine Learning, Artificial Intelligence, Ophthalmology, Privacy
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