Optometric ManagementEmerging Technologies in Eye Care 

🎓 Expert Commentary / Peer Perspective

Teleretina programs at the VA and Indian Health Service have operated for more than two decades, establishing infrastructure that is now informing broader AI adoption for autonomous image reading. The presenter argued that AI’s greatest scale impact will occur at institutional and national levels rather than in private practice, with downstream effects reaching community optometrists as patients arrive having already undergone machine learning-based image analysis.

Clinical Considerations:

• LumineticsCore (Digital Diagnostics) is the first FDA De Novo-cleared autonomous AI system for diabetic retinopathy detection; additional tools address glaucomatous optic neuropathy and oculomics-based systemic risk
• AI turnaround time for image reading is immediate versus days-to-weeks for human-operated teleretina reporting, positioning AI as a scale solution for high-volume screening programs
• Consumer LLMs (e.g., ChatGPT) are being used by patients for retinal image interpretation; the presenter demonstrated failed lesion detection in a diabetic retinopathy case, underscoring hallucination risk
• Clinicians should anticipate seeing patients whose prior imaging has already been processed by institutional AI pipelines, with morbidity and mortality risk flags generated before the office visit

Practice Applications:

• Recognize that AI bias and LLM limitations require clinicians to be equipped for evidence-backed patient conversations about what automated tools can and cannot detect
• Monitor oculomics developments as AI-derived systemic risk profiling from retinal images is an emerging area of investigation with implications beyond ocular disease
• Integrate awareness of teleretina frameworks (AMA telehealth playbook, vendor evaluation, team formation) before adopting new service delivery models
• Interpret autonomous AI diagnostic outputs as adjunctive tools, not replacements for clinical examination and judgment