Clinical performance of an interactive platform based on artificial intelligence in ophthalmology: experience in a third-level reference center

Abstract

Objective To assess the diagnostic performance of an interactive platform for ophthalmology in a real-world clinical setting at a tertiary care center. Methods A prospective, observational, cross-sectional study was conducted on consecutive patients referred by general practitioners to the Ophthalmology Department of a third-level University Hospital. Participants underwent automated ocular evaluation using DORIA ( Robotic Ophthalmological Diagnosis through Artificial Intelligence ) including the Eyelib™ Robotized scan (MIKAJAKI, Geneva, Switzerland). Results Of 2,774 referred patients, 2,478 (89.3%) attended their appointments and were examined. Among them, the mean age was 58.5 ± 14.5 years and 1,535 (61.9%) were women. Visual acuity loss with 591 (24.2%) patients and fundus examination 421 (17.3%) patients were the most common referral reasons. Based on DORIA results, ophthalmologists concluded that 807 patients (32.6%) required no further ophthalmological care, 858 (34.6%) needed follow-up with a general ophthalmologist, and 341 (13.8%) were referred to primary care. In a detailed assessment of 2,478 cases, 1,148 (46.3%) were discharged or referred to primary care, while 472 (35.5%) individuals required specialized ophthalmology care. Conclusion The platform might be considered as a valuable solution to the waiting list issue, reducing specialist interventions, and optimizing healthcare resources. Real-world findings suggest potential cost savings and improved patient management. Further studies are necessary to validate its comparative effectiveness.