“Artificial intelligence will not replace optometrists, but optometrists who understand AI will be better equipped to deliver efficient, data-driven, and proactive patient care,” said Jessilin Quint, OD, MBA, MS, FAAO, of Smart Eye Care in Augusta, Maine, in her presentation at Optometry's Meeting 2026 in Phoenix.
Dr. Quint explained that eye care is an ideal field for AI integration because it is imaging-rich with optical coherence tomography (OCT), fundus photography, and visual fields. Eye care is also ideal for AI because of its quantifiable metrics and patterns, as well as the high global burden of preventable vision loss, she said.
AI in Clinical Diagnosis and Screening
Dr. Quint discussed how AI is being used for retinal imaging and disease detection. For example, Digital Diagnostics’ LumineticsCore (formerly known as IDx-DR), the first US Food and Drug Administration (FDA) De Novo-cleared AI diagnostic system, can autonomously diagnose diabetic retinopathy in people living with diabetes.
For glaucoma, Dr. Quint said visual field progression analysis and optic nerve head assessment using AI-enhanced OCT is currently in the pipeline.
For corneal and anterior segment analysis, Dr. Quint discussed a solution that is also in the pipeline to detect keratoconus using AI-integrated topography. AI for dry eye disease analysis is available and helps optometrists streamline a treatment plan by analyzing large clinical datasets, she said.
AI-Powered Tools in the Exam Room
Among the AI-powered tools that Dr. Quint highlighted for the exam room are decision support systems, such as AI-assisted triage and referral systems, as well as clinical decision support tools that are embedded in electronic medical records.
Dr. Quint also pointed out how AI is being used in refractive and cataract surgery—for example, IOL calculation using AI algorithms, AI-assisted topography and biometry data interpretation, and predictive models for refractive outcomes and complications.
Practice Management and Operational Efficiency
Dr. Quint explained that AI can be used effectively in scheduling and patient flow optimization—for example, predictive appointment scheduling can reduce no-show rates and improve clinic throughput.
AI can also be used for patient engagement and follow-up. For this, Dr. Quint suggested using chatbots and virtual assistants, patient education videos using an avatar, and remote monitoring of chronic conditions using home tonometry and AI vision tests.
Regarding financial and administrative integration, Dr. Quint recommended automated billing, coding, and documentation assistance, as well as analytics-driven business decisions (eg, demand forecasting and inventory).
Challenges and Barriers to Implementation
Among the challenges and barriers Dr. Quint pointed out were technical and data limitations, including data privacy, algorithm bias, and a lack of diverse datasets. She also stressed the need for high-quality annotated datasets. She also said that clinical trust and acceptance (eg, concerns about diagnostic autonomy and balancing AI recommendations with clinical judgment) present additional challenges, and noted regulatory and legal considerations, including FDA/European Medicines Agency approval pathways for AI tools and liability in AI-guided misdiagnosis.
Ethical and Humanistic Considerations
Regarding patient consent and transparency, Dr. Quint said that patients should be informed of AI use in their care and that it should be used to ensure informed decision-making and respect for autonomy.
Dr. Quint added that AI should be used as augmentation, not a replacement for clinicians, and that maintaining empathy, communication, and trust is key.
“Understanding AI is becoming as important as understanding imaging technology; clinicians must be prepared to critically evaluate these tools while maintaining oversight, empathy, and patient trust,” she said.
The Future of AI in Everyday Eye Care
Looking ahead to the future, Dr. Quint said personalized medicine and predictive care will include genomics and AI for risk stratification and early intervention, along with adaptive treatment plans using real-time data. For education and skill development, she suggested training eyecare professionals to work with AI systems and integrating AI literacy into medical and optometry curricula.
Finally, Dr. Quint explained that AI will reshape—but not replace—clinical eye care, and that early adoption and critical evaluation are key. She also stressed that eyecare professionals must lead ethical, patient-centered integration.
"The future of eye care is not human vs machine, it is clinicians leveraging technology to provide more personalized, precise, and accessible care,” she concluded. OM
Dr. Quint reports no relevant disclosures.