Artificial intelligence in the diagnosis and management of refractive errors Journal Article
| Authors: | Nguyen, T.; Ong, J.; Jonnakuti, V.; Masalkhi, M.; Waisberg, E.; Aman, S.; Zaman, N.; Sarker, P.; Teo, Z. L.; Ting, D. S. W.; Ting, D. S. J.; Tavakkoli, A.; Lee, A. G. |
| Article Title: | Artificial intelligence in the diagnosis and management of refractive errors |
| Abstract: | Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied on comprehensive eye examinations by eye care professionals, but access to these specialized services has remained limited in many areas of the world. Given this, artificial intelligence (AI) has shown immense potential in transforming the diagnosis and management of refractive error. We review AI applications across various aspects of refractive error care – from axial length prediction using fundus images to risk stratification for myopia progression. AI algorithms can be trained to analyze clinical data to detect refractive error as well as predict associated risks of myopia progression. For treatments such as implantable collamer and orthokeratology lenses, AI models facilitate vault size prediction and optimal lens fitting with high accuracy. Furthermore, AI has demonstrated promise in optimizing surgical planning and outcomes for refractive procedures. Emerging digital technologies such as telehealth, smartphone applications, and virtual reality integrated with AI present novel avenues for refractive error screening. We discuss key challenges, including limited validation datasets, lack of data standardization, image quality issues, population heterogeneity, practical deployment, and ethical considerations regarding patient privacy that need to be addressed before widespread clinical implementation. © The Author(s) 2025. |
| Keywords: | review; outcome assessment; follow up; prediction; risk assessment; standardization; algorithm; image quality; artificial intelligence; disease exacerbation; visual impairment; eye examination; virtual reality; telehealth; machine learning; myopia; electronic health record; deep learning; best corrected visual acuity; refraction error; deep neural network; photorefractive keratectomy; digital technology; large language model; chatgpt; refractive error; laser in situ keratomileusis; laser-assisted subepithelial keratectomy; refractive surgery |
| Journal Title: | European Journal of Ophthalmology |
| Volume: | 35 |
| Issue: | 4 |
| ISSN: | 11206721 |
| Publisher: | The Author(s) 2025 |
| Date Published: | 2025-01-01 |
| Start Page: | 1456 |
| End Page: | 1480 |
| Language: | English |
| DOI: | 10.1177/11206721251318384 |
| PUBMED: | 40223314 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Review -- Source: Scopus |
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