Integrating artificial intelligence in strabismus management: current research landscape and future directions

Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresenta...

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Bibliographic Details
Published in:Experimental biology and medicine (Maywood, N.J.) N.J.), 2024-11, Vol.249, p.10320
Main Authors: Wu, Dawen, Huang, Xi, Chen, Liang, Hou, Peixian, Liu, Longqian, Yang, Guoyuan
Format: Article
Language:English
Subjects:
Artificial Intelligence
Deep Learning
Experimental Biology and Medicine
eye care
Humans
large model
multimodal
strabismus
Strabismus - diagnosis
Strabismus - therapy
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Summary:Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresentation of diverse strabismus types and reliance on single-source data remain prevalent. Emphasizing the need for inclusive AI systems, future research should focus on expanding AI capabilities with large model technologies, integrating multimodal data to bridge existing gaps, and developing integrated management platforms to better accommodate diverse patient demographics and clinical scenarios.
ISSN:1535-3699
1535-3702
1535-3699
DOI:10.3389/ebm.2024.10320