ReviewTaiwan journal of ophthalmology
Artificial intelligence in oculoplastics: Novel applications and future prospects.
Review in Taiwan journal of ophthalmology. 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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into ophthalmic subspecialties, encompassing oculoplastics, is rapidly evolving. AI has demonstrated significant promise in enhancing diagnostic accuracy, streamlining clinical workflows, and personalizing surgical decisionmaking. This review aims to summarize current applications of AI in oculoplastics, identify major challenges limiting its broader adoption, and explore future directions for research and clinical translation. We provide a narrative review of peer-reviewed studies and recent developments in AI-assisted diagnosis, image analysis, surgical planning, and patient monitoring within the domain of oculoplastics. Emerging AI applications include automated detection of eyelid tumors, facial analysis for ptosis and orbital disorders, preoperative planning, and postoperative outcome assessment. However, challenges such as limited data diversity, lack of interpretability, regulatory barriers, and ethical considerations persist. AI holds great promise in augmenting oculoplastic care. Moving forward, multidisciplinary collaboration, clinical validation, and advances in multimodal learning will be key to realizing the full potential of AI in this field.
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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.