# Metastatic cancer detection and management with artificial intelligence and augmented reality (Review)

## TL;DR

This review explores how artificial intelligence and augmented reality are improving detection and treatment of metastatic cancer, while addressing challenges in implementation.

## Contribution

The paper highlights novel applications of AI and AR in metastatic cancer care and identifies barriers to their clinical adoption.

## Key findings

- AI tools enhance early detection and personalized treatment by integrating multimodal data.
- AR improves surgical precision and patient education through real-time visualization.
- Challenges include algorithmic bias, data quality, privacy, and regulatory issues.

## Abstract

Metastatic cancer remains a significant global health challenge, contributing to the majority of cancer-related mortality due to late detection, therapeutic resistance and the complexity of disseminated disease. Recent advances in artificial intelligence (AI) and augmented reality (AR) are transforming the landscape of metastatic cancer detection and management. AI-driven tools, including radiomics, deep learning models, and predictive analytics, enhance early identification of metastatic lesions, improve diagnostic accuracy, and support personalized treatment strategies by integrating multimodal clinical, imaging and molecular data. At the same time, AR technologies are increasingly applied in image-guided surgery, real-time tumor visualization and patient education, enabling more precise interventions and improved clinical decision-making. The combined use of AI and AR fosters multidisciplinary collaboration, facilitates comprehensive treatment planning, and may ultimately improve patient outcomes. However, despite these advancements, several challenges limit widespread implementation, including algorithmic bias, variability in data quality, concerns regarding patient privacy, and regulatory and ethical constraints. Furthermore, integration into clinical workflows requires robust validation, clinician training, and standardized guidelines. Future efforts are required to focus on developing transparent, generalizable AI models, strengthening data-security frameworks, and enhancing AR usability to ensure equitable, safe, and effective incorporation of these emerging technologies into metastatic cancer care.