The number of primary open angle glaucoma (POAG) cases will increase by 250% by 2050, directly affecting over 7 million lives. Development of high-quality, accessible, and cost-effective strategies for eye care for these individuals is critically important. POAG is 4-5 times higher in Blacks, progresses more rapidly, and appears about 10 years earlier compared to those of European descent. This proposal evaluates a novel screening approach developed from our prior CDC-funded glaucoma detection study (AL-SIGHT), utilizing a hybrid type 2 implementation science design. This will enable evaluating feasibility of program expansion occurring simultaneously with a comparative effectiveness trial of the intervention. This implementation study will evaluate a modified streamlined version of our AI-SIGHT screening protocol by including a validated artificial intelligent algorithm to provide interpretation of ocular imaging and clinical data obtained within Federally Qualified Health Centers (FQHCs) located in medically underserved regions. Using this program within FQHC’s will facilitate potential expansion nationwide. These centers provide primary health care services for lower income people and treat more than 27 million people yearly at over 12,000 sites. Moreover, we are targeting patients at-risk for glaucoma living in areas with large Black populations. Our prior research in FQHC’s has demonstrated a higher prevalence of POAG at FQHCs than when screening in the general population. This high “pre-test” probability addresses critical concerns expressed by the US Preventative Screening Task Force (USPSTF) that population screening is not merited. Moreover, patients in AL-SIGHT demonstrated a high rate of follow-up (at 76%) to recommended eye examination due to the integration of FQHC’s into their local communities. We have developed and tested a novel multimodal telemedicine approach in our current CDC- funded AL-SIGHT study within FQHCs, with good agreement with expert diagnosis and adherence to follow-up. Patients were satisfied with the screening. Yet this approach is limited in expandability in that it still requires expert physician guidance. With the new protocol we will determine the ability of validated AI algorithms to make screening diagnoses, previously shown to provide diagnostically equivalent recommendations to physician diagnoses, reducing the cost of implementation. The study will identify POAG screening rates, adherence to follow-up care rates, cost effectiveness of the protocol, and acceptability for primary care provider and patients. If our outcomes are successful, we will develop a scalability plan for FQHCs. Our long-range goal is to reduce the personal burden of POAG in medically underserved areas.