Artificial intelligence (AI) is becoming an increasingly important part of modern medicine, and ophthalmology is particularly well suited to its use. Eye care generates large amounts of measurable information—from retinal photographs and optical coherence tomography (OCT) images to visual field results and other functional testing data.
AI systems can analyze these datasets for patterns that may be difficult or time-consuming to identify manually. As a result, researchers and clinicians are exploring AI for disease screening, image interpretation, glaucoma assessment, visual field analysis, clinical decision support, and workflow improvement.
Some applications have already entered clinical practice, while many others remain under active research and validation.
1. Diabetic Retinopathy Screening
Diabetic retinopathy is one of the most established areas for AI use in ophthalmology.
AI algorithms can analyze retinal photographs for abnormalities associated with diabetic retinopathy and determine whether a patient may require further evaluation. Certain systems have received regulatory authorization for autonomous screening, allowing them to provide a screening result without an ophthalmologist first interpreting the retinal image.
This approach may be particularly valuable in primary-care settings and communities where immediate access to an ophthalmologist or retina specialist is limited.
By helping identify patients who require specialist attention, AI-assisted screening has the potential to expand access while allowing eye-care professionals to focus their time on patients who need more comprehensive evaluation.
2. Retinal Imaging and OCT Analysis
Retinal imaging produces large quantities of detailed clinical information, making it a natural area for machine-learning applications.
Researchers have developed AI models capable of evaluating fundus photographs, OCT scans, and OCT angiography images. These systems can be trained to recognize anatomical abnormalities and biomarkers associated with retinal disease.
Research has examined AI applications involving conditions such as:
- Diabetic retinopathy
- Diabetic macular edema
- Age-related retinal disease
- Retinal vascular abnormalities
- Other structural changes affecting the retina
A growing area of research involves using AI to quantify imaging biomarkers rather than simply determining whether disease is present.
For example, algorithms may help measure or identify changes across multiple imaging examinations, potentially giving clinicians additional information when evaluating disease severity or response to treatment.
Recent research indexed by PubMed highlights the growing role of AI in analyzing OCT and OCT angiography data and identifying disease-related retinal biomarkers.
3. Supporting Glaucoma Assessment
Glaucoma presents a particularly interesting challenge for artificial intelligence because diagnosis and management often depend on several different types of clinical information.
An ophthalmologist may consider:
- Optic nerve appearance
- Retinal nerve fiber layer measurements
- OCT findings
- Intraocular pressure
- Visual field results
- Changes observed across multiple visits
AI models have therefore been investigated for glaucoma detection, classification, staging, risk assessment, and progression analysis.
Rather than relying on a single measurement, future AI-assisted systems may be able to evaluate multiple sources of structural and functional information together.
Research has already demonstrated the potential of machine-learning and deep-learning models for interpreting glaucoma-related imaging and visual field data. A review available through PubMed discusses the expanding use of artificial intelligence in glaucoma detection and progression assessment.
4. AI and Visual Field Analysis
Visual field testing measures a patient’s sensitivity to visual stimuli at different locations within the field of vision. It remains an important component of glaucoma management, neuro-ophthalmic assessment, and other areas of eye care.
Unlike an image of the retina or optic nerve, a visual field provides functional information about how effectively a patient can see across different areas of vision.
AI researchers have explored whether patterns within visual field results can help identify glaucoma, classify visual field defects, and recognize changes that may indicate disease progression.
A systematic review published through PubMed examined deep-learning applications involving ophthalmic imaging and standard automated perimetry, including their potential use in glaucoma detection and progression analysis.
At the same time, visual field testing itself is becoming increasingly digital and portable. Technologies such as the VF2000 from Micro Medical Devices demonstrate how visual field examinations can now be performed using VR-based testing platforms.
The VF2000 is not an AI diagnostic system. However, the broader transition toward digital functional testing is significant because structured clinical data may become increasingly useful as AI-assisted analysis continues to develop.
5. Monitoring Disease Over Time
Many ophthalmic conditions cannot be evaluated accurately from a single examination.
Glaucoma, retinal disease, and other chronic conditions often require clinicians to compare results from multiple visits to determine whether meaningful progression has occurred.
This presents another potential application for artificial intelligence.
Instead of evaluating one test in isolation, AI systems can potentially analyze longitudinal datasets containing months or years of information. Researchers are investigating whether these systems can identify subtle patterns that may indicate disease progression earlier or more consistently.
In glaucoma, for example, researchers have studied AI models using combinations of structural imaging and visual field information to evaluate disease status and progression.
However, this remains an evolving area of research. Predicting exactly how an individual patient’s disease will develop is considerably more complex than detecting abnormalities within a single test.
AI-generated predictions therefore need appropriate clinical validation before they can be relied upon for individual treatment decisions.
6. Clinical Decision Support
Another important application of AI is clinical decision support.
Rather than attempting to replace the ophthalmologist, decision-support systems are designed to provide additional information that may assist the clinician.
An AI system might, for example:
- Flag an image containing a possible abnormality
- Identify measurements outside an expected range
- Compare findings with previous examinations
- Highlight patterns associated with particular diseases
- Prioritize patients who may require closer evaluation
These applications can be particularly useful when clinicians are reviewing large amounts of clinical information.
The distinction between decision support and autonomous diagnosis is important. In most areas of ophthalmology, AI should be viewed as an additional analytical tool rather than a substitute for comprehensive clinical judgment.
The ophthalmologist remains responsible for interpreting the available information in the context of the individual patient.
7. Improving Clinical Efficiency
Not every valuable application of artificial intelligence involves detecting disease.
Healthcare professionals also spend significant amounts of time managing documentation, organizing patient information, reviewing large datasets, and performing repetitive administrative tasks.
AI technologies are increasingly being developed to assist with areas such as:
- Clinical documentation
- Data organization
- Image prioritization
- Workflow automation
- Patient triage
- Research data analysis
Reducing repetitive work could allow physicians and clinical staff to spend more time interacting directly with patients.
A 2026 review indexed by PubMed discusses the growing role of AI across ophthalmic diagnosis, clinical decision support, and workflow optimization.
What Are the Limitations of AI in Ophthalmology?
Despite rapid progress, artificial intelligence still has important limitations.
An AI system is heavily influenced by the data used to develop and train it. If a dataset does not adequately represent different patient populations, disease presentations, imaging devices, or clinical environments, the system’s performance may not translate equally well to other settings.
Important challenges include:
- Quality and diversity of training data
- Validation across different patient populations
- Compatibility between different clinical devices
- Data privacy and cybersecurity
- Regulatory requirements
- Algorithm transparency
- Integration with existing clinical workflows
Another challenge is explainability. Some advanced AI systems can produce highly accurate classifications while providing limited information about how they reached their conclusion.
For physicians making clinical decisions, understanding why a system produced a particular result may be just as important as the result itself.
For these reasons, rigorous clinical validation remains essential before new AI technologies become part of routine patient care.