Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.
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- Record sourced from PubMed, PMID 42106570.
- Also identified by DOI 10.1038/s41746-026-02755-7.
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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.