Telehealth Artificial Intelligence Applications and Workflows Between Primary Care and Ophthalmologist: A Pilot Study

Diabetic Retinopathy (DR) remains a leading cause of preventable blindness worldwide. Despite clear screening guidelines, referral adherence from primary care to ophthalmology is suboptimal. Telehealth-enabled Artificial Intelligence (AI) workflows offer a potential solution for closing this gap. The purpose of this study is to assess the feasibility of a telehealth referral pathway between Primary Care Providers (PCPs) and an ophthalmologist using AI-enabled diabetic eye screening. A pilot program implemented an AI-based referral workflow at a single site. Retinal imaging was performed at point-of-care using a non-mydriatic camera, processed using AI software (AEYE-DS) and securely transmitted to a subspecialty ophthalmologist. Referral outcomes, turnaround time and clinical decision impacts were recorded. Out of 47 patients (18 male, 29 female) screened, 16 (34.0%) were flagged for referral. Of the 13 patients referred to the lead ophthalmologist (BPB, a comprehensive ophthalmologist), 7 (53.8%) completed ophthalmology follow-up. The AI triage system reduced referral turnaround time and identified significant pathology including cataracts and retinal abnormalities. The integration of AI-supported imaging, secure transmission and structured referral workflows between PCPs and ophthalmologists is feasible and may improve adherence to DR screening guidelines.