AI in Ophthalmology: Advancing Eye Care With AI Vision Technology

As an ophthalmology professional, you know that eye care can run on images. Multimodal AI examines multiple image types simultaneously to aid in the early detection of diseases.¹ Sound intriguing? You can introduce artificial intelligence (AI) to your practice with a small project and simple ideas to help patients move forward with advanced AI diagnostics.

AI Vision Technology Explained

Eye care teams often rely on optical coherence tomography (OCT) scans, fundus photos, visual fields and more to understand what’s happening inside the eye. Multimodal AI vision technology combines these images, allowing you to identify patterns that are more difficult to spot when viewed individually. Early studies suggest that combining image types may help support early detection and confident decision-making, especially in complex cases.¹

Early reports of AI in ophthalmology success

In a 2024 review in Eye and Vision, researchers reported that multimodal ophthalmic AI based on deep learning showed “excellent diagnostic efficacy” across several eye diseases compared with single-modality models.¹ 

New foundation models further advance this idea. A 2025 study in npj Digital Medicine introduced EyeCLIP, a multimodal visual-language model trained on 2.77 million ophthalmology images from 11 modalities plus partial clinical text, demonstrating strong performance across multiple benchmark datasets.² These tools aren’t drop-in replacements, but they may point toward practical ways AI could support everyday decisions in your practice.

Steps to implement AI in your ophthalmology practice

Busy clinics can test AI by designing a small, time-limited project focused on a single question, such as triage for diabetic retinopathy, or identifying patients who may need closer follow-up. A pilot project can give your team a safe way to see how AI fits into real workflows before making bigger commitments. 

1. Pilot the AI ophthalmology program

During the pilot project, review every AI suggestion and track simple metrics, such as re-scan rates, time spent reviewing images or how often AI prompts a change in follow-up plans. Use your observations to understand where the AI tool adds value and where it may need adjustment.

2. Plan the introduction of AI ophthalmology technology

As you plan, consider how offering patient financing options can help patients manage the cost of AI-enabled diagnostics. Recent economic evaluations of AI-assisted ophthalmic screening programs show that clinics may incur expenses for software licensing, image-analysis steps and added imaging workflows, which could raise per-patient diagnostic costs.³

3. Review the process with patients

Think ahead to how you can explain why an AI-supported test is recommended, what out-of-pocket costs may apply and how payment options, including patient financing, can help them move forward with recommended testing. Building these conversations into your early planning might make AI-related testing feel more predictable for patients and easier for staff to explain.

Is Your Practice Ready for AI Ophthalmology Technology?

A readiness check may help you see whether your current workflows, imaging and team structure can support a small AI pilot project. It may also give you a clear picture of the technical steps you can take before comparing AI vendors. Follow these steps to ensure your practice is ready to implement AI optometry technology.

1. Map your imaging and data flow

Here's an example of the steps you may want to include when setting up your readiness check:

• Begin by listing the image types your practice already captures and how they’re stored. These could include OCT, fundus photos, ultra-widefield (UWF) images, anterior-segment photos, visual fields, corneal topography or tomography. Note which devices are networked, how images are labeled and whether you can export data in formats such as Digital Imaging and Communications in Medicine (DICOM) or standard image files.
• Look at how your electronic medical record (EMR) and image management system handles these files. You don't need a perfect technology stack to begin, but you do need a consistent way to move de-identified images to an AI tool and review the results.²

2. Clarify roles and review steps

Define who will lead the project and who will review AI outputs when the lead clinician is away.

Decide who will:

• Confirm image quality before submitting to AI
• Review AI outputs and compare them with their own interpretation
• Document when AI suggestions were accepted, modified or rejected

These steps help maintain human oversight during the early phases of AI use.²

3. Prepare for patient conversations

Plan how staff will explain any new AI-supported tests that may incur out-of-pocket costs. Staff should understand when a new imaging step is optional, how much it typically costs and how to explain payment options, including any patient financing solutions you offer.

Clear, early conversations may help prevent delays during busy clinic days. It may also help to decide when to mention payment options, including patient financing tools, so your staff knows how to answer patient questions regarding costs.

Design a Simple Pilot for Your Clinic Flow

Keep the scope narrow on your AI pilot project. This may help your staff minimize disruption, manage concerns and see early signs of what supports your workflow.

1. Choose your use case

Start by picking a focused use case, such as:

• Triage for diabetic retinopathy based on fundus photos plus OCT
• Risk stratification for glaucoma progression using visual fields and optic nerve imaging
• Identifying patients who may need closer follow-up for age-related macular degeneration

2. Add to workflow

Once you pick your use case, map where AI will fit into your current workflow. For many practices, the most natural spot may be after technicians complete imaging but before the physician's exam. 

Consider adding AI to a pre-visit review step, allowing doctors to view AI flags alongside images when opening the chart. This is just one way to fit AI into existing routines. Try to introduce AI at a point in the workflow where it can help support daily tasks.

3. Select metrics

For your pilot, commit to a short list of simple metrics you can track without extra software. This could include:

• Time saved or added per patient for image review
• Re-scan rates if AI helps flag poor-quality images
• Number of cases where AI prompts a change in follow-up plans
• Qualitative feedback from clinicians on trust and usefulness

Remember, the results of your AI pilot should be easy to find, view and understand.

Safety, Bias and Monitoring

Patient safety should stay at the center of any AI project. That starts with choosing tasks that support decisions you already make. These could include triage suggestions or image quality, tasks that staff can efficiently double-check using AI.

Keep a simple AI log for each case during the pilot. At minimum, record the imaging used, what the AI tool suggested, the clinician’s final decision and any follow-up steps taken. Monthly case reviews can help you spot accuracy drift, mismatches with clinician judgment or differences in performance across patient groups.

Beware of bias

Once a month, review a sample of cases from the log. Look for patterns where AI flags findings you disagree with, or where it misses problems you see in the images. If your practice serves a diverse patient population, pay attention to whether AI performance appears consistent across different age groups or disease stages.

Emerging medical imaging AI regulations

AI is a relatively new field, and regulators and researchers are working on better ways to evaluate medical imaging AI. For example, the FDA’s Digital Health Center of Excellence maintains an AI-Enabled Medical Device List. It identifies AI-enabled devices authorized for marketing in the United States and describes how this list can support transparency for clinicians and patients.⁴ 

The agency also highlights regulatory science tools, such as the MIC-MET Tree. This is a decision tree-based tool that may help developers and researchers select appropriate performance metrics for AI and machine learning classification algorithms in medical imaging.⁵

These tools help underscore the importance of tracking performance over time. You may want to build a rollback plan that allows you to decide ahead of time when to pause or modify AI use — for example, if your log shows a drop in agreement between clinician judgment and AI outputs or if a software update changes model behavior in ways you do not expect.

Patient Communication and Team Training

Using AI may raise patient questions, especially in a field as personal as eye care, so it’s important to coach your team on the terminology used. You may find it helpful to describe AI as a “second set of eyes” that reviews imaging and supports the doctor’s decision-making, rather than as a replacement for them. Emphasize that the ophthalmologist or optometrist still examines every patient and makes the final call.

Update your intake and consent materials

Next, update your intake or consent materials with one short paragraph about AI. Keep it in plain language and consider covering these three basics:

• Your practice may use software to analyze eye images.
• This software is designed to support, not replace, your eye care professional.
• Patients can ask questions about how their data and images are used.

Add a few items to your technology checklist for staff to review during examinations or imaging. For example, explain which images you are taking and why they matter for that visit.

Clarify payment and test options

If an AI-linked test is optional and not covered by insurance, you may want to state that clearly before proceeding. Also, outline payment expectations, including whether you accept a patient financing solution like the CareCredit credit card to help patients manage out-of-pocket costs. Resources such as vision financing fundamentals can help your team feel prepared to discuss payment choices when patients ask about ways to manage costs.

Standardize team messaging

Consider scripting a short, two- or three-sentence explanation your technicians can use. This keeps messaging simple and consistent across the team. During training, remind your team that if a patient has detailed questions about AI or test necessity, staff should refer to the prescribing doctor.

For more ideas on positioning and patient expectations, review how other optometry practices compete with online retail and balance technology, convenience and value in their messaging.

Budget Smart for AI in Ophthalmology

Your eye care practice may not be ready to replace major equipment just to try AI.

Structure your AI budget around milestones rather than long-term contracts. A short pilot period, such as 60 to 90 days, can allow your team to observe how an AI tool may fit into daily tasks. 

Simple metrics from your AI log can show whether the tool supports efficiency, reduces re-scan rates or helps clinicians make clearer follow-up decisions. After reviewing your own results, you can decide whether to expand, renegotiate or pause.

Clear cost communication may also be important when new AI-enabled diagnostics result in out-of-pocket expenses.

What’s Next for AI in Ophthalmology

Foundation models are beginning to support more tasks across retina, glaucoma and corneal disease, with early research showing how multimodal approaches can help unify information from several imaging types.¹ New models such as EyeCLIP demonstrate how these tools may handle a wide range of classification and analysis tasks as they continue to develop.²

Clinicians who want to stay current can follow FDA updates, including the agency’s AI-Enabled Medical Device List that identifies AI-enabled tools authorized for marketing in the U.S.⁴ Professional societies, including the American Academy of Ophthalmology, regularly publish education and updates that can help clinicians stay informed as AI tools evolve.

Looking Forward With AI Vision Technology

A small, well-structured pilot project may give your practice a steady way to explore multimodal AI while keeping clinician oversight. Clear roles, simple metrics and consistent patient communication may help your team understand where these tools add value. As AI-enabled diagnostics evolve, thoughtful planning and clear cost discussions, including available payment options, can support smoother adoption and help patients follow through with recommended care.

A Patient Financing Solution for Ophthalmologists

Cost may be a barrier to care for your current and prospective ophthalmology patients. You can help them manage the cost of the care they want or need by offering the CareCredit credit card as a financing solution. CareCredit allows patients to pay for their eye exams, LASIK, surgeries and other treatments over time while helping to enhance the payments process for your practice.

When you accept CareCredit, patients can see if they prequalify with no impact to their credit score, and those who apply, if approved, can take advantage of special financing on qualifying purchases.* Additionally, you will be paid directly within two business days.

Learn more about the CareCredit credit card as a patient financing solution for your ophthalmology practice or start the provider enrollment process by filling out this form.

Author Bio

Sarita Harbour is a freelance writer with more than 15 years of experience covering personal finance, consumer banking, small business banking and credit for online audiences. Her work has appeared on sites such as Forbes, TIME/MONEY, MSN, The Motley Fool, First Horizon Bank, Investopedia and more. 

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