Artificial Intelligence and Eye Disease—Bridging Innovation and Clinical Practice

Haoyu Chen; Louis Arnould; Andrzej Grzybowski

doi:10.3390/jcm15166237

August 12, 2026Journal of Clinical MedicineOpen Access

Artificial Intelligence and Eye Disease—Bridging Innovation and Clinical Practice

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Key Points

To evaluate the current progress, real-world utility, and practical challenges of translating artificial intelligence innovations into routine ophthalmic clinical practice.
Reviewed developments in artificial intelligence algorithms designed for automated ophthalmic disease screening and diagnosis.
Evaluated clinical validation studies, implementation barriers, and regulatory frameworks shaping clinical adoption.
Artificial intelligence models demonstrate high diagnostic accuracy across major ocular conditions, including diabetic retinopathy and glaucoma.
Effective clinical deployment requires overcoming real-world integration hurdles, algorithmic bias across diverse populations, and regulatory requirements.

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Implication

Clinical review demonstrates progress in translating artificial intelligence into eye care, highlighting pathways to bridge technological innovation and routine clinical workflows.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec79cb70b84ec8b914231 https://doi.org/10.3390/jcm15166237

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Artificial Intelligence and Eye Disease—Bridging Innovation and Clinical Practice

Key Points

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Implication

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Artificial Intelligence and Eye Disease—Bridging Innovation and Clinical Practice

Key Points

No abstract yet

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Implication

Discussion

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