Artificial intelligence applications in pediatric ophthalmology: A comprehensive review - Anupama Janardhanan; Meenakshi Ravindran; Allapitchai Fathima; Neelam Pawar (Journal of Clinical Ophthalmology and Research)
Pediatric ophthalmology presents unique challenges due to the ongoing development of children’s eyes and their limited ability to articulate symptoms, complicating diagnosis and treatment. The global shortage of pediatric ophthalmologists underscores the urgent need for innovative solutions to enhance access to care. Recent advancements in artificial intelligence (AI) and machine learning are poised to transform this field by improving diagnostic accuracy, refining treatment strategies, and expanding care accessibility. This review examines the current applications of AI in pediatric ophthalmology, synthesizing findings from various studies to illuminate both the promising opportunities and the challenges that remain. A comprehensive literature search was conducted across databases such as PubMed, Google Scholar, and Ovid MEDLINE, focusing on peer-reviewed articles that discuss AI’s role in screening, diagnosis, and therapeutic applications. The review highlights AI’s potential to automate disease detection, enhance efficiency through telemedicine, and facilitate early intervention, particularly in conditions such as retinopathy of prematurity and congenital cataracts. Furthermore, it explores existing AI models and their applicability in diagnosing refractive errors and strabismus. Despite the significant promise of AI, challenges such as data availability, diversity, and integration into clinical workflows persist. The future of AI in pediatric ophthalmology appears bright, with ongoing research aimed at optimizing algorithms, personalizing treatment, and expanding telemedicine capabilities. Ultimately, AI is set to revolutionize pediatric eye care, improving outcomes for children worldwide while complementing the expertise of healthcare professionals.