Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine
Document Type
Article
Publication Date
2-6-2025
Abstract
Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI's potential in advancing the field of ophthalmology and improving patient care.
Recommended Citation
Kumar, Rahul; Ong, Joshua; Waisberg, Ethan; Lee, Ryung; Nguyen, Tuan; Paladugu, Phani; Rivolta, Maria C.; Gowda, Chirag; Janin, John V.; Saintyl, Jeremy; Amiri, Dylan; Gosain, Ansh; and Jagadeesan, Ram, "Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine" (2025). SKMC Student Presentations and Publications. Paper 74.
https://jdc.jefferson.edu/skmcstudentworks/74
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Language
English
Comments
This article is the author’s final published version in Bioengineering, Volume 12, Issue 2, 2025, Article number 156.
The published version is available at https://doi.org/10.3390/bioengineering12020156. Copyright © 2025 by the authors.