# AI-driven strategies for advancing corneal cell therapy: a promising frontier

## TL;DR

AI is transforming corneal cell therapy by improving accuracy and enabling personalized treatments.

## Contribution

The paper highlights how AI can address challenges in corneal cell therapy through novel data-driven strategies.

## Key findings

- AI improves corneal cell therapy by identifying biomarkers and optimizing cell delivery.
- AI-based methods offer higher accuracy and efficiency compared to conventional techniques.
- AI applications span preclinical to clinical stages in corneal cell therapy.

## Abstract

Cell-based therapies offer an alternative to corneal transplantation for the management of corneal diseases. However, these approaches require a deeper understanding of the principles of cell therapy, and the ability to predict and diagnose outcomes pre- and post-operatively is highly desirable. Recently, the development of innovative techniques that leverage predefined data from multiple cohorts with corneal diseases has received considerable attention. Approaches using artificial intelligence (AI) can address major concerns in corneal cell therapy, including the identification of novel biomarkers, improvements in cell delivery processes, and the acceleration of personalized treatments. This review summarizes real-world examples of AI applications from preclinical through clinical studies, with a focus on corneal cell-based therapies.

A schematic illustrating the evolution of corneal cell therapy from conventional methods to AI-enabled approaches. Conventional cell therapies may have lower accuracy due to missing prerequisites, such as large, high-quality datasets. By contrast, AI is expected to improve corneal cell therapy. AI-based methods, which offer advantages in accuracy, speed, and quality, hold strong promise for corneal cell therapy applications.Comparison infographic between future AI-driven corneal cell therapy and conventional methods. The left side describes AI algorithms for pre-clinical applications, including cell source selection and characterization, aiming for efficient therapy with cost-effectiveness and accuracy. The right side details conventional methods like optical microscopy and slit-lamp analysis, highlighting challenges such as high cost and low accuracy. A central image of the cornea links both sections.

A schematic illustrating the evolution of corneal cell therapy from conventional methods to AI-enabled approaches. Conventional cell therapies may have lower accuracy due to missing prerequisites, such as large, high-quality datasets. By contrast, AI is expected to improve corneal cell therapy. AI-based methods, which offer advantages in accuracy, speed, and quality, hold strong promise for corneal cell therapy applications.