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Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.

Publication ,  Journal Article
Lee, E; Hunt, D; Cakir, Y; Kuo, D; Zhou, Z; Pajic, M; Hadziahmetovic, M
Published in: Surv Ophthalmol
September 18, 2025

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's "black-box" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.

Duke Scholars

Published In

Surv Ophthalmol

DOI

EISSN

1879-3304

Publication Date

September 18, 2025

Location

United States

Related Subject Headings

  • Ophthalmology & Optometry
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry
 

Citation

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Lee, E., Hunt, D., Cakir, Y., Kuo, D., Zhou, Z., Pajic, M., & Hadziahmetovic, M. (2025). Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment. Surv Ophthalmol. https://doi.org/10.1016/j.survophthal.2025.09.007
Lee, Euna, David Hunt, Yavuz Cakir, David Kuo, Ziqi Zhou, Miroslav Pajic, and Majda Hadziahmetovic. “Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Surv Ophthalmol, September 18, 2025. https://doi.org/10.1016/j.survophthal.2025.09.007.
Lee E, Hunt D, Cakir Y, Kuo D, Zhou Z, Pajic M, et al. Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment. Surv Ophthalmol. 2025 Sep 18;
Lee, Euna, et al. “Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Surv Ophthalmol, Sept. 2025. Pubmed, doi:10.1016/j.survophthal.2025.09.007.
Lee E, Hunt D, Cakir Y, Kuo D, Zhou Z, Pajic M, Hadziahmetovic M. Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment. Surv Ophthalmol. 2025 Sep 18;
Journal cover image

Published In

Surv Ophthalmol

DOI

EISSN

1879-3304

Publication Date

September 18, 2025

Location

United States

Related Subject Headings

  • Ophthalmology & Optometry
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry