Five-year cost-effectiveness of AI for adult diabetic eye exams—a health system perspective

Teleophthalmology and artificial intelligence (AI) -based retinal screening have emerged as scalable approaches to improve detection of diabetic retinal disease (DRD), yet their economic value within U.S. health systems is not well defined. We developed a 5-year Markov model to evaluate the cost-effectiveness of these alternative strategies against screening by an eye care professional (ECP) across two scenarios: a small primary care network and a large integrated health system with Willingness-to-Pay (WTP) ranges, respectively, of $400–1500 and $800–3500 per patient over 5 years. AI-based strategies yield 3 times more screenings completed, 3.6–3.8 times more true positives, and 7.5–8.0 times more patients who initiate treatment than ECP. Teleophthalmology strategies are moderately effective. The most cost-effective strategy for each scenario depends on health system scale and WTP. For a primary care network with $400 WTP, teleretinal via handheld camera is preferred for sites screening ≤ 2282 patients; handheld AI otherwise. At $1500 WTP, teleretinal via handheld camera is preferred for sites with ≤ 884 patients, handheld AI for 885–1476, and stationary AI otherwise. In an integrated health system at $800 WTP, ECP dominates across volumes of 250–14,000 patients. At $3500 WTP, teleretinal via handheld is preferred for ≤ 1393 patients per site; stationary AI otherwise. AI-based strategies, particularly handheld AI, are cost-effective with larger patient volumes and higher WTP thresholds. Their cost-effectiveness is driven by real-time diagnostic feedback that enables earlier detection and reduces costs associated with managing advanced forms of DRD.