Eye banking and corneal transplantation · 2026
Agentic Artificial Intelligence in Eye Banking: A Proposed Workflow.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Karmakar Ranit, Kiros Elizabeth Kassahun, Nath Siddharth, Keane Pearse A, Kumar Krishna, Lohmeier John, Sikder Shameema, Meinecke Eric, Hanna Chris, Eghrari Allen O
Аннотация
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.
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.