What's one specific way you've successfully implemented AI tools in your optometric practice? How did it change your diagnostic approach or patient care?

One specific way we've incorporated AI into our practice is through automated analysis in routine eye testing, such as refraction and visual field exams. The system helps identify patterns or inconsistencies in patient responses, which is especially useful when results are borderline or need closer review. This adds an extra layer of reliability to everyday assessments without changing the core clinical process. Evidence from the National Eye Institute also highlights how AI can support more accurate and consistent eye care decision-making. What this has changed is how decisions are validated rather than made. Instead of relying on a single test result, there is an added level of confirmation before finalizing prescriptions or next steps. It has also improved how findings are explained to patients, making discussions clearer and more structured. Overall, AI has not replaced clinical judgment but has strengthened consistency, confidence, and patient understanding in routine care.

Possibly the most effective way to bring AI into your optometric practice currently would be through AI based retinal image analysis built right into your OCT and fundus camera workflow. Automated systems can identify early diabetic retinopathy, macular degeneration and glaucoma related nerve fiber thinning in less than 30 seconds per scan. For the practice seeing 25-40 patients daily, that translate into your doctor entering the exam room with a risk report already prepared. AI identifies microaneurysms and drusen deposits before they are apparent to human eyes, even highly trained ones scanning thousands of data points in one layer of the retina.

The most significant shift was in how I read corneal topography. Anterior segment imaging has always been central to my practice. The corneal map of a keratoconus patient, the pachymetry data ahead of a refractive procedure, the endothelial cell counts informing a transplant decision. These are the clinical conversation. What AI-assisted analysis changed was the quality of pattern recognition available within it. The specific application was subclinical keratoconus. Cases where topography looks almost normal, where indices sit just within accepted parameters, where a less scrutinised map would pass unremarked. AI surfaced asymmetries that aggregate into a meaningful signal before any single parameter crosses a threshold. That earlier identification changed management decisions in ways that mattered to the patients involved. The language around this requires care though. AI sharpens clinical attention. It surfaces patterns for the surgeon to interrogate. The judgment about what a finding means for a specific person, their lifestyle, their occupation, their tolerance for risk, remains entirely human. AI raised the baseline from which scrutiny begins. Understanding its boundaries is as important as understanding its capabilities.

One specific way we successfully implemented AI in our optometric practice is through AI-powered retinal imaging analysis. I call this the "second set of eyes" approach. The AI scans patient retinal images for early signs of conditions like diabetic retinopathy or glaucoma, highlighting subtle patterns that might be missed in a routine exam. For example, after integrating this tool, we began detecting early-stage eye diseases weeks to months sooner than before. This allowed us to start preventive care earlier, personalize treatment plans, and counsel patients with more confidence. Patients noticed the thoroughness, and follow-up compliance improved. The takeaway: AI doesn't replace the clinician it amplifies diagnostic precision, enabling earlier intervention, more personalized care, and a stronger trust relationship with patients.

One practical way AI has been integrated into eye care is through retinal image analysis. Many modern imaging systems now include AI software that reviews retinal scans and highlights early indicators of conditions such as diabetic retinopathy, glaucoma risk, or macular degeneration. In the past, providers would manually study each scan in detail, which required time and a high level of concentration, especially during busy clinic days. AI systems now assist by scanning the images almost instantly and flagging subtle patterns or abnormalities that deserve a closer look. The clinician still makes the final judgment, but the technology works like an additional set of eyes that helps prioritize what needs immediate attention. The impact on patient care has been meaningful because potential concerns can be identified earlier and discussed more clearly with patients. When imaging software highlights a small retinal change that might otherwise be easy to overlook, it opens the door to early intervention and better long term outcomes. At Davila's Clinic, conversations about new medical technologies often center on how tools like AI can support clinical decision making while still keeping the provider patient relationship at the center of care. Technology works best when it frees up time for deeper conversations, allowing providers to explain findings, answer questions, and guide patients through the next steps in protecting their health.

There is a practical use case of AI for retinal imaging to help identify early signs of disease such as diabetic retinopathy or macular degeneration. The use of AI does not take away from the clinical judgement of the physician but rather provides an additional layer of review that is efficient and consistent, particularly in high-volume clinical environments. The change in the detection of disease has been in timing and confidence. AI provides an early detection of subtle patterns that may have otherwise required manual review to detect which allows the development of a more proactive dialogue regarding the disease with the patient. In addition, using AI in the diagnostic process allows for a more organized approach to documenting findings and provides assistance to the physician with respect to patient care.

One effective application of AI in optometric practice has been the use of AI-assisted retinal imaging for early detection of conditions such as diabetic retinopathy. Research published in Nature Medicine found that AI systems can match or exceed specialist-level accuracy in identifying retinal diseases, significantly improving early diagnosis rates. This approach shifts diagnostics from reactive to proactive, enabling earlier interventions and reducing the risk of vision loss. A practical takeaway is that AI delivers the most value when integrated into routine screening workflows, where it enhances clinical precision without disrupting patient experience.

One impactful implementation of AI in optometric care has been the deployment of AI-powered retinal screening tools to assist in early detection of diabetic retinopathy and other vision-threatening conditions. According to research published in The Lancet Digital Health, AI models have demonstrated sensitivity levels exceeding 90% in identifying retinal diseases, enabling faster and more consistent screening outcomes. This has shifted the diagnostic approach from episodic assessments to continuous, data-driven evaluation, allowing earlier intervention and improved patient outcomes. A key takeaway is that AI delivers the greatest value when embedded within routine diagnostic workflows, enhancing clinical accuracy without adding complexity.

One effective use of AI in optometric care has been the integration of AI-assisted retinal screening to detect conditions such as diabetic retinopathy at an early stage. Research published in Nature Medicine highlights that AI systems can achieve diagnostic accuracy comparable to specialists, with sensitivity levels exceeding 90%. This has enabled a shift from reactive diagnosis to proactive screening, allowing earlier intervention and improved patient outcomes. A key lesson is that AI creates the most value when embedded into routine workflows, enhancing diagnostic precision without disrupting clinical processes.