[Artificial intelligence in diabetic retinopathy screening]

Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological data, the combined prevalence of bilateral blindness and severe visual impairment among individuals aged 50 years and older is approximately 0.9%, and international estimates suggest that around 90% of vision loss cases could be prevented or treated with appropriate care. However, the coverage of ophthalmic screening remains low, primarily due to the lack of targeted financing, limited ophthalmology workforce capacity, and the absence of a unified national screening protocol.

The aim of our study is to review the professional, organizational, financial, legal and ethical conditions for the implementation of artificial intelligence-supported ophthalmic screening in Hungary, with a particular focus on diabetic retinopathy.

We conducted a targeted narrative literature review of national epidemiological, human resource, and cost data, as well as an analysis of international diabetic retinopathy screening models and the European Union regulatory frameworks for medical devices and artificial intelligence, using sources selected based on clinical and public health relevance.

The level of Hungarian ophthalmological screening practice is insufficient to significantly reduce the burden of preventable vision impairment, primarily due to limited human resources and funding constraints. The current human resource capacity of the Hungarian ophthalmic care system is insufficient to provide the approximately one million diabetic fundus examinations required annually according to professional guidelines. Preventive and screening activities are not organized as dedicated services but are largely delivered as part of routine ophthalmic outpatient care, without separate financing. International experience indicates that the use of artificial intelligence as a decision-support or triage tool can reduce specialist workload while maintaining diagnostic accuracy.

Artificial intelligence-supported fundus screening systems have the potential to improve access to screening, consistency, and efficiency. The introduction of artificial intelligence-based fundus screening in Hungary would require the establishment of appropriate financing mechanisms, regulation of task-sharing involving optometrists and allied health professionals, and compliance with relevant regulatory and ethical frameworks. A transitional hybrid model - combining the pilot use of an internationally validated artificial intelligence system in parallel with launch of domestic development - may offer a realistic pathway toward a structured national screening program and contribute to reducing the disease burden of preventable blindness. Orv Hetil. 2026; 167(22): 865-875.