Agentic Artificial Intelligence in Eye Banking: A Proposed Workflow.

PURPOSE: To develop an agentic artificial intelligence (AI) framework that streamlines and standardizes eye bank operations by automating donor screening, image analysis, and tissue suitability assessment under expert supervision.

METHODS

A modular, on-premises, multi-agent system was designed consisting of three AI agents. The Donor Screening Agent analyzes medical histories to identify contraindications for ocular donation. The Corneal Image Analysis Agent interprets multimodal images, including specular microscopy, slit-lamp, and optical coherence tomography, to quantify endothelial cell density, morphology, and clarity. The Suitability Assessment Agent integrates outputs from both preceding agents to generate structured recommendations for transplantation, research use, or rejection. Each agent operates locally to ensure privacy, real-time processing, and compliance with health information regulations.

RESULTS

The proposed architecture replaces fragmented manual workflows with a unified, intelligent system that reduces variability, accelerates evaluations, and enhances transparency. By enabling consistent and rapid analysis of donor data and corneal images, the system can improve tissue utilization and decrease discard rates. A modular, low-latency design supports deployment in diverse settings, including low- and middle-income countries, where connectivity and infrastructure may be limited.

CONCLUSIONS

Agentic AI offers a scalable pathway toward a standardized eye banking process by combining automation with human oversight. This approach has the potential to improve decision quality, operational efficiency, and equity in corneal transplantation. It carries implications for both limited resource settings and the broader context of transplantation medicine.