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Purpose of Review <p>This article explores the evolving role of artificial intelligence (AI) in neuro-oncology, highlighting its potential to enhance diagnostic accuracy, predict patient outcomes, optimize treatment planning, and streamline clinical workflows.</p> Recent Findings <p>AI applications have led to significant advancements in automated tumor segmentation, molecular classification, risk stratification, treatment response evaluation, and computational pathology. AI-driven innovations have also accelerated drug discovery and leveraged natural language processing to generate structured clinical reports and extract actionable insights from unstructured data.</p> Summary <p>AI has transformative potential in neuro-oncology; however, challenges like data quality, model generalizability, and clinical integration persist. Overcoming these barriers may involve new computational techniques and hardware efficiencies, as well as raising awareness, fostering interdisciplinary education, and expanding access to AI-driven tools.</p>

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Emerging Trends in Artificial Intelligence in Neuro-Oncology

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Saahil Chadha,
  • Durga V. Sritharan,
  • Thomas Hager,
  • Rahul D’Souza,
  • Sanjay Aneja

摘要

Purpose of Review

This article explores the evolving role of artificial intelligence (AI) in neuro-oncology, highlighting its potential to enhance diagnostic accuracy, predict patient outcomes, optimize treatment planning, and streamline clinical workflows.

Recent Findings

AI applications have led to significant advancements in automated tumor segmentation, molecular classification, risk stratification, treatment response evaluation, and computational pathology. AI-driven innovations have also accelerated drug discovery and leveraged natural language processing to generate structured clinical reports and extract actionable insights from unstructured data.

Summary

AI has transformative potential in neuro-oncology; however, challenges like data quality, model generalizability, and clinical integration persist. Overcoming these barriers may involve new computational techniques and hardware efficiencies, as well as raising awareness, fostering interdisciplinary education, and expanding access to AI-driven tools.