Artificial intelligence for full-cycle clinical management in orthokeratology: a comprehensive review
- 1School of Medicine, Shenzhen University, Shenzhen, China
- 2Department of Ophthalmology, Shenzhen Second People's Hospital, Shenzhen, China
Abstract
The global myopia epidemic, especially among children, poses a major public health burden. Orthokeratology (OK) is an established intervention for slowing myopia progression, yet its conventional practice is constrained by variable treatment responses, an experience-dependent fitting process, and the need for vigilant long-term safety monitoring. Artificial intelligence (AI) offers an approach to enhance precision, efficiency, and accessibility across the entire OK care cycle. This review assesses AI applications in OK, encompassing pre-treatment efficacy prediction, AI-assisted lens fitting and automated fit assessment during treatment, and post-treatment safety monitoring with complication risk alert. Evidence indicates that AI models perform comparably to, and in some tasks surpass, human experts in predicting outcomes and determining optimal lens parameters. However, translation from research to routine practice is hindered by limited data quality and diversity, the opacity of complex algorithms, insufficient external validation, and unresolved ethical concerns. Progress depends on building large-scale, diverse datasets, developing interpretable and robust models, and fostering effective human-AI collaboration.
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BibTeX
@article{wang2026artificial,
title = {{Artificial intelligence for full-cycle clinical management in orthokeratology: a comprehensive review}},
author = {Xiaotong Wang and Haojiang Yang},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1950432},
url = {https://doi.org/10.3389/fmed.2026.1950432},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence for full-cycle clinical management in orthokeratology: a comprehensive review
AU - Xiaotong Wang
AU - Haojiang Yang
PY - 2026
DA - 2026/10/12
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1950432
UR - https://doi.org/10.3389/fmed.2026.1950432
AB - The global myopia epidemic, especially among children, poses a major public health burden. Orthokeratology (OK) is an established intervention for slowing myopia progression, yet its conventional practice is constrained by variable treatment responses, an experience-dependent fitting process, and the need for vigilant long-term safety monitoring. Artificial intelligence (AI) offers an approach to enhance precision, efficiency, and accessibility across the entire OK care cycle. This review assesses AI applications in OK, encompassing pre-treatment efficacy prediction, AI-assisted lens fitting and automated fit assessment during treatment, and post-treatment safety monitoring with complication risk alert. Evidence indicates that AI models perform comparably to, and in some tasks surpass, human experts in predicting outcomes and determining optimal lens parameters. However, translation from research to routine practice is hindered by limited data quality and diversity, the opacity of complex algorithms, insufficient external validation, and unresolved ethical concerns. Progress depends on building large-scale, diverse datasets, developing interpretable and robust models, and fostering effective human-AI collaboration.
SN - 2296-858X
ER -