Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning
Year: 2026
Type: article
Abstract: 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.
Source: npj Digital Medicine
Institutions: Second Affiliated Hospital of Zhejiang University, Zhejiang University, China University of Mining and Technology, National University of Singapore, Nanyang Technological University, Tan Tock Seng Hospital, Singapore Eye Research Institute, Duke-NUS Medical School, National Healthcare Group, University of Pennsylvania, Foundation for the Development of Ophthalmology, University of Warmia and Mazury in Olsztyn
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Award: G1083066802
OpenAlex ID: W7160715181