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<p>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.</p>

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Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月10日

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  • Kai Jin,
  • Kaikai Zhao,
  • Rupesh Agrawal,
  • Gui-Shuang Ying,
  • Andrzej Grzybowski

摘要

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.