Purpose <p>Patients with diabetic retinopathy (DR) are at risk of visual deterioration owing to systemic and financial barriers in accessing appropriate care. DR screening tools that implement artificial intelligence (AI) algorithms are gaining recognition due to their accuracy and high-throughput potential. This systematic literature review aimed to understand the economic, humanistic, and clinical burden associated with delayed DR management and the impact of AI-based screening tools for diagnosis and treatment.</p> Methods <p>MEDLINE, Embase, and Cochrane Library databases were searched per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (1 January 2014 to 28 October 2024). Screening, extraction, and quality assessment were performed by two independent reviewers. Supplementary searching was conducted to evaluate visual outcomes.</p> Results <p>In total, 33 records were included. Economic evidence demonstrated that infrequent screening was initially cost-saving but decreased patient quality-adjusted life years, delayed sight-threatening DR diagnosis, and resulted in high treatment-related costs in the long term. Several studies found delayed DR treatment to adversely impact visual acuity, central subfield thickness, and time spent with vision loss. The majority of economic studies evaluating AI-based screening found its use to result in lower overall costs than conventional screening, while two noted higher costs attributable to greater screening uptake and increased specialist referrals. Most studies that modeled clinical impact found AI-based screening to reduce blindness or vision loss versus conventional screening.</p> Conclusions <p>This research underscored the considerable harms associated with delayed DR diagnosis and treatment. AI-based screening tools have the potential to become powerful instruments in supporting improved clinical outcomes for patients and economic benefits for healthcare systems.</p>