How Artificial Intelligence is Changing Retina Care

August 28, 2026

Artificial intelligence is no longer simply a futuristic concept in medicine. In retinal care, AI is rapidly becoming a powerful tool for analyzing the enormous amount of information contained within retinal images and helping physicians detect, quantify, and monitor disease with increasing precision.

The retina is uniquely suited to artificial intelligence because much of modern retinal medicine is built around sophisticated imaging. Optical coherence tomography (OCT), fundus photography, OCT angiography, fundus autofluorescence, and widefield retinal imaging can produce highly detailed images of the eye—often revealing microscopic changes that would be impossible to appreciate with a conventional examination alone. AI algorithms can analyze these images at a scale and speed that would be difficult for a human observer to replicate.

One of the most established applications of AI in ophthalmology is the detection of diabetic retinopathy. AI-based systems can analyze retinal photographs and identify patients who may have clinically significant diabetic retinal disease and require further evaluation. The technology has progressed beyond the research laboratory into real-world clinical use, including FDA-cleared autonomous screening systems. In 2024, for example, the FDA cleared an AI-enabled handheld retinal imaging system capable of autonomously screening for diabetic retinopathy, illustrating how AI can potentially bring retinal screening into settings where access to an ophthalmologist may otherwise be limited.

Artificial intelligence is also generating considerable excitement in age-related macular degeneration (AMD), one of the most common conditions treated by retina specialists. Researchers are developing algorithms capable of identifying AMD, classifying its severity, measuring drusen and areas of geographic atrophy, and analyzing subtle structural changes within the retina. AI is also being investigated as a way to predict which patients may progress to advanced AMD and which patients may require treatment in the future.

OCT imaging may be one of the most transformative areas of AI-assisted retina care. An OCT scan contains thousands of individual measurements across the retina, allowing physicians to visualize fluid, retinal thickness, pigment epithelial detachments, atrophy, and other microscopic abnormalities. AI can help segment and quantify these features, potentially allowing a retina specialist to more objectively track disease over time and assess a patient's response to treatment. In wet AMD and diabetic macular edema, for example, AI-based analysis can assist with identifying and measuring retinal fluid—one of the most important biomarkers used when determining whether treatment is working.

Perhaps even more exciting is the possibility of moving from simply detecting disease to predicting what will happen next. Researchers are investigating whether AI can identify patterns in retinal images that indicate which patients are more likely to develop advanced AMD, geographic atrophy, or recurrent retinal fluid. Other models are being studied for their ability to predict treatment response and help physicians determine how frequently a patient may need therapy. These applications could ultimately move retina care toward a more personalized model in which treatment decisions are increasingly informed by an individual patient's unique retinal characteristics.

Geographic atrophy, an advanced form of dry AMD, is another area where AI may prove particularly valuable. Because geographic atrophy can enlarge gradually over time, accurately measuring its progression is important. Recent research has demonstrated that deep-learning systems can analyze multiple forms of retinal imaging to detect geographic atrophy, measure its extent, and investigate the likelihood of future progression. As treatments designed to slow geographic atrophy have become available, the ability to detect and monitor these changes accurately has become increasingly important.

The potential impact extends beyond diagnosis. AI may eventually help retina specialists integrate information from multiple sources—including retinal images, previous examinations, treatment history, and other clinical data—to create a more complete picture of an individual patient's disease. Rather than simply asking, "What does the retina look like today?" the goal is increasingly to understand, "How is this retina changing, and what is most likely to happen next?"

That distinction is important. The future of AI in retina care is not necessarily about replacing the retina specialist. It is about augmenting the specialist's ability to see, measure, compare, and predict. AI can process enormous quantities of imaging data, but clinical care still requires interpretation within the context of the individual patient. Symptoms, medical history, examination findings, imaging quality, treatment history, and patient preferences all remain essential components of medical decision-making. Current reviews emphasize that AI is best viewed as a clinical support tool rather than a replacement for ophthalmologists.

There are also important challenges. AI systems must be rigorously validated across different patient populations, imaging devices, and clinical environments. Issues such as bias, data quality, transparency, privacy, and the ability to explain how an algorithm reaches a particular conclusion remain active areas of research. An algorithm that performs exceptionally well in one dataset may not necessarily perform the same way in every clinical setting.

For patients, however, the direction is clear: retinal medicine is becoming increasingly quantitative, data-driven, and personalized. The same imaging technology that allows a retina specialist to see microscopic changes in the retina can now be analyzed with increasingly sophisticated computational tools. As these technologies continue to mature, AI may help physicians detect disease earlier, monitor progression more precisely, identify patients at greatest risk, and tailor treatment more effectively.

The most exciting aspect of artificial intelligence in retina care may therefore not be the technology itself, but what it could make possible for patients: earlier detection, more precise monitoring, and increasingly individualized treatment designed to preserve vision for as long as possible.

At MARIN RETINA, we remain committed to incorporating advances in retinal imaging, diagnostics, and treatment as they become clinically validated and appropriate for our patients. While AI will not replace the expertise and judgment of a retina specialist, it represents one of the most promising technologies shaping the future of retinal medicine.