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Artificial Intelligence in Health

PERSPECTIVE ARTICLE
Artificial intelligence for ophthalmic drug

discovery and development: Capabilities,
applications, and challenges

Siddharth Gandhi 1 and Michael Balas *
2
1 School of Medicine, Queen’s University, Kingston, Ontario, Canada
2 Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, Ontario, Canada

Abstract
The integration of artificial intelligence (AI) into ophthalmic drug discovery and
development presents transformative opportunities to address the inherent
complexities and challenges of creating targeted therapies for eye diseases. The
ability of AI to process vast datasets can facilitate the discovery of novel drug
candidates, improve predictions of drug efficacy and safety, and streamline the drug
development pipeline. Applications can range from enhancing target identification
and compound screening to refining predictive toxicology. However, challenges
such as data limitations, computational demands, model interpretability, and
ethical considerations remain. Despite these hurdles, the integration of AI with
emerging technologies and its potential to optimize clinical trials signifies a new era
of innovation in ophthalmology, emphasizing its critical role in addressing current
challenges and advancing therapeutic development. In this paper, we explore the
role of AI in ophthalmic drug discovery, highlighting its potential to address critical
*Corresponding author:
Michael Balas challenges in the field and delineating its impact across various stages of drug
(michael.balas@mail.utoronto.ca) development.
Citation: Gandhi S, Balas M.
Artificial intelligence for ophthalmic
drug discovery and development: Keywords: Ophthalmology; Artificial intelligence; Drug discovery; Drug development
Capabilities, applications, and
challenges. Artif Intell Health.
2024;1(3):26-30.
doi: 10.36922/aih.3341
1. Introduction
Received: April 1, 2024
The field of ophthalmology encounters unique challenges in drug development
Accepted: May 13, 2024
stemming from the complexity of eye diseases and the necessity for highly targeted
Published Online: July 22, 2024 therapeutic interventions. Traditional drug discovery processes are often slow and
Copyright: © 2024 Author(s). costly, and they are hampered by high failure rates, particularly in predicting drug
This is an Open-Access article efficacy and safety within the context of eye diseases. These challenges underscore
1
distributed under the terms of the the urgent need for innovative approaches to accelerate and refine drug discovery in
Creative Commons Attribution
License, permitting distribution, ophthalmology.
and reproduction in any medium,
provided the original work is Artificial intelligence (AI) is reshaping the frontier of drug discovery. By analyzing
properly cited. large datasets, AI can efficiently identify patterns and insights that may lead to the
Publisher’s Note: AccScience discovery of novel drug candidates, as well as enhance predictions of drug efficacy
Publishing remains neutral with and toxicities. Moreover, AI can enhance our understanding of disease mechanisms,
regard to jurisdictional claims in
published maps and institutional contribute to personalized medicine, and streamline drug development pipelines,
affiliations. thereby expediting the process, reducing costs, and ultimately increasing effectiveness. 2

Volume 1 Issue 3 (2024) 26 doi: 10.36922/aih.3341