Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective.
Artificial intelligence (AI) tools are rapidly reshaping ophthalmology by improving screening and diagnosis for diabetic retinopathy, age-related macular degeneration, glaucoma, and increasingly retina-based systemic risk assessment. This narrative review provides a comparative assessment of regulatory pathways governing ophthalmic AI and software as a medical device (SaMD) across the United States, European Union, United Kingdom, Australia, China, Japan, Canada, India, and selected emerging jurisdictions. We used a structured search of public regulator databases, guidance documents, manufacturer disclosures, and peer-reviewed literature to assemble a representative sample of marketed or authorized devices through August 2025; the device inventory is illustrative rather than exhaustive. Key differences persist in device classification, evidence expectations, change management for adaptive algorithms, and post-market oversight. Examples such as LumineticsCore, EyeArt, DrNoon for CVD, CLAiR, and EyeWisdom illustrate how risk-based approaches vary across jurisdictions. These inconsistencies can delay multi-region deployment and complicate implementation, supporting the need for lifecycle-focused and internationally aligned standards for safe, transparent, and equitable use of ophthalmic AI.
Authors
- Xia Gong
- Marko M. Popovic (ORCID: https://orcid.org/0000-0002-0370-5968)
- Polat Göktaş (ORCID: https://orcid.org/0000-0001-7183-6890)
- Ally Zhao
- Ryo Kawasaki
- Kai Jin
- Hidenori Takahashi
- Mingguang He
- T Y Alvin Liu
- Andrzej Grzybowski
- Masahiro Miyake
Institutions
- Sabancı Üniversitesi (TR)
- Hong Kong Polytechnic University (HK)
- Johns Hopkins University (US)
- University of Toronto (CA)
- Kyoto University (JP)
- Vision Eye Institute (AU)
- Second Affiliated Hospital of Zhejiang University (CN)
- University of Warmia and Mazury in Olsztyn (PL)
- The University of Osaka (JP)
Publication Details
- Journal
- PubMed
- Published
- 2026-05-08
- DOI
- https://doi.org/10.1016/j.apjo.2026.100328
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00