# Data-driven strategies for immunoradiotherapy in uveal melanoma: the role of artificial intelligence

## TL;DR

This paper explores how artificial intelligence can improve immunoradiotherapy for uveal melanoma by enabling personalized treatment strategies through data-driven insights.

## Contribution

The paper provides a comprehensive review of AI applications in immunoradiotherapy for uveal melanoma, highlighting novel strategies for personalized treatment optimization.

## Key findings

- AI models can accurately predict therapeutic outcomes in uveal melanoma patients.
- AI enables quantitative characterization of the tumor immune microenvironment.
- AI supports personalized optimization of radiotherapeutic strategies.

## Abstract

Uveal melanoma (UM) represents the most common primary intraocular malignancy in adults and remains a formidable clinical challenge due to its high metastatic potential and characteristically limited response to conventional systemic therapies. While the combination of radiotherapy and immunotherapy has emerged as a promising multimodal strategy for managing this complex malignancy, its efficacy is significantly constrained by profound individual variations in tumor biology, immune microenvironment composition, and dynamic treatment response patterns. In recent years, artificial intelligence (AI) has fundamentally transformed the landscape of precision oncology by enabling sophisticated image analysis, robust data-driven prediction, and seamless integration of heterogeneous multi-omics information. Within the specific context of uveal melanoma, AI-driven computational models have demonstrated significant potential to accurately predict therapeutic outcomes, quantitatively characterize the tumor immune microenvironment, and optimize radiotherapeutic strategies on a personalized basis. This comprehensive review critically examines and synthesizes recent progress in AI applications for immunoradiotherapy in uveal melanoma, systematically exploring their transformative potential to refine diagnostic accuracy, enhance treatment precision, and ultimately improve long-term patient outcomes through intelligent, data-driven personalized medicine approaches that bridge multiple disciplinary boundaries.