Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning
Recent advances in ophthalmic AI have improved benchmark performance, yet clinical trust remains limited. We argue that progress should move beyond data and model scaling toward trustworthy, skill-efficient systems that integrate multimodal evidence, external knowledge, and uncertainty-aware reasoning. Ophthalmology provides a strong testbed for agentic AI, but safe clinical translation will require rigorous validation, workflow integration, and evaluation frameworks aligned with real-world decision making.
Authors
- Kaikai Zhao
- Rupesh Agrawal
- Andrzej Grzybowski (ORCID: https://orcid.org/0000-0002-3724-2391)
- Gui-Shuang Ying
- Kai Jin
Institutions
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- China University of Mining and Technology (CN)
- Tan Tock Seng Hospital (SG)
- Zhejiang Technical Institute of Economics (CN)
- Singapore Eye Research Institute (SG)
- Duke-NUS Medical School (SG)
- Second Affiliated Hospital of Zhejiang University (CN)
- University of Warmia and Mazury in Olsztyn (PL)
- University of Pennsylvania (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-05-09
- DOI
- https://doi.org/10.1038/s41746-026-02755-7
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China