ReviewNPJ digital medicine2026
Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.
Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.