article · Medicina
Artificial intelligence, incorporating machine learning and deep learning, presents powerful capabilities for diagnosing and managing retinal conditions. These technological approaches address the complexity and variability of diseases such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, and sickle cell retinopathy, among others. By evaluating models and performance metrics, these digital tools demonstrate clear potential to enhance screening efficiency and enable earlier disease detection. However, practical integration into clinical environments remains constrained by critical operational barriers. These challenges include the opaque nature of algorithmic decision-making known as the black box phenomenon, inherent biases within underlying datasets, and difficulties in capturing holistic patient assessments. Successfully addressing retinal conditions requires a collaborative model where artificial intelligence functions to support and augment human clinicians rather than replace them, working to reduce healthcare disparities and improve overall patient outcomes.
Retinal diseases can cause severe vision impairment if not identified early. Deploying artificial intelligence alongside clinicians helps improve screening efficiency and diagnosis across conditions such as diabetic retinopathy and macular degeneration. Understanding the diagnostic capabilities and operational hurdles of these digital tools is essential for establishing safe, unbiased clinical systems that broaden access to timely, high-quality eye care.
The applications focus on diagnostic screening algorithms and clinical decision-support tools for eye care practitioners and health systems. Because the work reviews existing models, performance metrics, and clinical hurdles such as data bias and algorithm opacity, the abstract indicates an early to intermediate stage of translation rather than an immediate, routine market deployment.
AI-generated from the published abstract. Always read the original work before citing.
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/medicina60040527
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
