ObjectiveRecent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition. DesignCross-sectional study.Subjects, Participants, and/or Controls: Retrospective dataset from the INSIGHT Health Data Research Hub at Moorfields Eye Hospital in London, United Kingdom, including 306,651 patients (602,826 eyes) with suspected retinal disease who underwent optical coherence tomography (OCT) imaging between 1 January 2008 and 10 April 2023. Methods, Intervention, or TestingA deep learning model was trained on OCT scans to identify patients potentially eligible for GA trials, using AI-generated segmentations of retinal tissue. This method's efficacy was compared against a traditional keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value (PPV) of this approach, by comparing AI predictions to expert assessments. Main Outcome MeasuresThe primary outcomes included the PPV of AI in identifying trial-eligible patients, and the secondary outcome was the intraclass correlation between GA areas segmented on FAF by experts and AI-segmented OCT scans. ResultsThe AI system shortlisted a larger number of eligible patients with greater precision (1,139, PPV: 63%; 95% CI: 54–71%) compared to the EHR search (693, PPV: 40%; 95% CI: 39–42%). A combined AI-EHR approach identified 604 eligible patients with a PPV of 86% (95% CI: 79–92%). Intraclass correlation of GA area segmented on FAF versus AI-segmented area on OCT was 0.77 (95% CI: 0.68–0.84) for cases meeting trial criteria. The AI also adjusts to the distinct imaging criteria from several clinical trials, generating tailored shortlists ranging from 438 to 1,817 patients. ConclusionsThis study demonstrates the potential for AI in facilitating automated pre-screening for clinical trials in GA, enabling site feasibility assessments, data-driven protocol design, and cost reduction. Once treatments are available, similar AI systems could also be used to identify individuals who may benefit from treatment.