Exploring the Impact of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Esthesioneuroblastomas: A Comprehensive Review

TL;DR

This paper reviews how AI and ML can help diagnose and manage esthesioneuroblastomas, a rare and complex type of cancer.

Contribution

It provides a comprehensive review of AI and ML applications in ENB diagnosis and treatment, highlighting potential benefits and limitations.

Findings

AI and ML have shown promise in improving diagnostic accuracy for ENBs.

Current applications of AI in ENBs are limited and require further research.

Integration of AI could enhance multidisciplinary management of ENBs.

Abstract

Esthesioneuroblastomas (ENBs) present unique diagnostic and therapeutic challenges due to their rare and complex clinical presentation. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools in various medical specialties, revolutionizing diagnostic accuracy, treatment planning, and patient outcomes. However, their application in ENBs remains relatively unexplored. This comprehensive literature review aims to evaluate the current state of AI and ML technologies in ENB diagnosis, radiological and histopathological imaging, and treatment planning. By synthesizing existing evidence and identifying gaps in knowledge, this review aims to showcase the potential benefits, limitations, and future directions of integrating AI and ML into the multidisciplinary management of ENBs.

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